| WEBVTT | |
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| um today I want to talk about prompting | |
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| and uh prompting is kind of a new uh | |
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| Paradigm as of a few years ago with | |
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| interacting with models it's now kind of | |
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| the standard uh in doing so and | |
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| basically what we do is we encourage a | |
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| pre-trained model to make predictions by | |
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| providing a textual prompt specifying | |
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| the task to be done this is how you | |
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| always interact with chat GPT or | |
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| anything else like this | |
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| um so prompting fundamentals uh the way | |
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| that basic prompting works is you append | |
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| a textual string to the beginning of the | |
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| output and you complete it and exactly | |
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| how you complete it can be based on any | |
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| of the generation methods that we talked | |
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| about in the previous class uh you know | |
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| beam search it can be uh sampling it can | |
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| be MBR or self-consistency or whatever | |
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| else um so I I put in when a dog sees a | |
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| squirrel it will usually | |
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| um into gpt2 small which is a very small | |
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| language model says Be Afraid of | |
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| Anything unusual as an exception that's | |
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| when a squirrel is usually afraid to | |
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| bitee um so as you can see if the model | |
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| is not super great you get a kind of not | |
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| very great response also um but then I | |
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| CED it into gp2 XL and uh what it says | |
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| when a dog sees a squirrel it will | |
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| usually lick the squirrel it will also | |
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| touch its nose to the squirrel the tail | |
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| and nose if it can um which might be | |
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| true um one thing I I should note is | |
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| when I generated these I used uh like | |
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| actual regular ancestral sampling so I | |
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| set the temperature to one I didn't do | |
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| top feed didn't do top K or anything | |
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| like this so this is a raw view of what | |
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| the language model thinks is like | |
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| actually a reasonable answer um if I | |
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| modified the code to do something else | |
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| actually maybe I can I can do that that | |
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| right now but if I modified the code to | |
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| use a | |
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| different output we can actually see uh | |
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| the different result that we | |
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| get since I I have it here | |
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| anyway actually sorry I'll need to | |
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| modify the code on my my screen here | |
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| um so I will | |
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| set uh top K to 50 top P to | |
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| 0.95 so you see I I changed the | |
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| generation parameters | |
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| here and I'll uh run all of | |
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| them you can see the uh the result that | |
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| we get in a little bit but basically um | |
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| so this is the standard method for | |
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| prompting I intentionally use gpt2 small | |
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| and gpt2 XL here because these are raw | |
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| based language models they were just | |
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| pre-trained as language models and so | |
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| when we prompt them we're getting a | |
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| language model that was just trained on | |
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| lots of texts view of what is likely | |
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| next text um there are other ways to | |
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| train language models like instruction | |
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| tuning and rlf which I'm going to be | |
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| talking in future classes and if that's | |
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| the case you might get a different | |
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| response here so when a dog sees a | |
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| squirrel it will usually get angry | |
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| scratched the squirrel and run off uh | |
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| some dogs may also attempt to capture | |
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| the squirrel or attempt to eat it dogs | |
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| will often to pick up the squirrel and | |
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| eat it | |
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| for it was more uh more violent than I | |
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| expected any um | |
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| so but anyway I think that like actually | |
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| you can see that when I used the | |
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| different generation parameters it | |
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| actually gave me something that was | |
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| maybe more typical than lick so lick is | |
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| maybe a kind of unusual uh answer here | |
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| but anyway | |
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| cool | |
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| so that's the basic idea of prompting we | |
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| tend to use prompting to try to solve | |
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| problems also so it's not just to | |
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| complete text although completing text | |
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| is useful and important like I complete | |
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| text in my Gmail all the time uh you | |
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| know it it's constantly giving me | |
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| suggestions about what I should write | |
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| next and I do tab autoc complete um you | |
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| know on your phone you're doing that | |
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| that's also using a language model um | |
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| but very often we'll use prompting to do | |
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| things other than just completing Texs | |
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| and when we do this uh this is kind of | |
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| the standard workflow for how we solve | |
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| NLP tasks with prompting the way we do | |
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| this is we fill in a prompt template | |
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| predict the answer and post-process the | |
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| answer in some | |
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| way so prompt templates are templates | |
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| where you will actually uh that you will | |
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| fill in with an actual input and so if | |
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| we have an input X which is something | |
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| like I love this movie our template will | |
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| be something like X overall it was Z or | |
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| overall it was and so if we do that when | |
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| we actually want to make a prediction we | |
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| will uh convert this into the actual | |
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| prompt we feed into the language model | |
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| by filling in the template um I love | |
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| this movie overall it was blank and then | |
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| fill this uh continuation | |
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| in a particular variety uh | |
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| that we use very broadly nowadays | |
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| because a lot of models are trained as | |
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| chatbots um but actually even if they're | |
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| not trained as chatbots this still works | |
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| to some extent um is a chat | |
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| prompt and so usually the way we we do | |
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| this is we specify inputs in a format | |
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| called the open AI messages format and | |
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| uh this is this is what it looks like | |
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| each we have a | |
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| list of outputs each list is given a | |
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| role and content and here so we have the | |
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| role of system and the content is please | |
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| classify movie reviews as positive or | |
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| negative uh then we have the role user | |
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| uh this movie is a | |
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| banger um and then we have roles uh | |
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| system message uh so is the roles we | |
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| have the system and the system is a | |
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| message provided to the system to | |
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| influence Its Behavior it's to explain | |
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| to it | |
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| like how it should be working um and so | |
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| you can see that this is explaining to | |
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| the system how it should be working user | |
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| is the message input by the user um and | |
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| so this could be just a single message | |
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| or if you have a multi-turn dialogue it | |
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| can be like user and then assistant and | |
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| then user and then assistant and then | |
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| user and then assistant and that makes | |
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| it clear that it's a multi-term dialogue | |
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| so if you have a multi-term dialogue in | |
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| chat GPT that's how they're feeding it | |
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| in um into the | |
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| system so what's happening behind the | |
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| scenes with these chat prompts basically | |
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| they're being converted into token | |
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| strings and then fed into the model so | |
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| despite the fact that this is fed in in | |
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| this format and it makes you think that | |
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| maybe something special is going on | |
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| actually in most cases these are just | |
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| being fed into the model uh as a prompt | |
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| so these are just kind of special | |
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| version of a uh of a template so here we | |
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| have um this is what the Llama template | |
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| looks like so basically you have um | |
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| square bracket ins and then for the | |
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| system message it's like um like angle | |
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| bracket uh angle bracket sis uh close | |
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| angle bracket close angle bracket and | |
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| then the actual system message and then | |
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| you have uh this closing out the system | |
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| message this closing out the instruction | |
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| then the user is surrounded by inst and | |
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| then the assistant is just like a | |
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| regular string so this is what the | |
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| actual textual string that's fed into | |
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| llama chat models is we can contrast | |
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| that to some other models so alpaka | |
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| looks like this um uh so we have like | |
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| hash instruction colon and then the | |
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| instruction for the user there there's | |
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| no distinction between system and user | |
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| so it's like hash instruction and then | |
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| the user message and then hash response | |
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| and then be assistant so it's not super | |
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| important which one we use here um the | |
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| important thing is that this matches | |
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| with what uh the model is trained and | |
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| I'll show you some example uh you know | |
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| I'll talk about that in more detail | |
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| later and we have a reference uh that I | |
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| got this uh | |
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| from and there's this toolkit that I um | |
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| I rather like recently it's called light | |
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| llm it makes it very easy to uh query | |
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| different llms uh and kind of like | |
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| unified things so basically you can | |
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| query many different types of LMS like | |
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| open AI or open source models or other | |
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| things like that and what happens behind | |
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| the scene is it basically takes um the | |
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| open AI messages format and converts it | |
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| into the appropriate prompt format for | |
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| whatever model you're using or the | |
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| appropriate API calls for whatever thing | |
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| you're using but | |
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| um this here basically | |
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| um if you click through this link shows | |
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| you okay this is what it looks like for | |
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| alpaca um so you have the instruction | |
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| instruction response this is what it | |
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| looks like for llama 2 chat this is what | |
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| it looks like for the oama um for AMA | |
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| this is what it looks like for mistol | |
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| and other things like that so you see | |
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| all of these are very similar but | |
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| they're like slightly different and | |
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| getting these right is actually kind of | |
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| important for the model doing a good | |
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| job | |
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| um any questions about | |
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| this yeah like say you start PR with | |
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| this um inut and then you started simar | |
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| without | |
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| model could you give an example yeah so | |
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| say um my account is a great movie or | |
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| this movie is great in front of I put | |
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| UMR | |
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| model | |
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| so depend it depends a lot on the | |
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| bottle the reason why this system | |
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| message was input here in the first | |
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| place was this wasn't originally a | |
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| feature of open AI models uh open AI was | |
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| the first place to introduce this which | |
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| is why I I'm calling it open ey messages | |
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| formul they didn't originally have | |
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| something like this but they were having | |
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| lots of trouble with um people trying to | |
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| reveal the prompts that were given to | |
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| systems uh like called like prompt | |
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| injection attacks or like jailbreaking | |
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| attacks or stff like that and so the | |
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| models would basically reveal this | |
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| prompt that was being used behind the | |
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| scenes by whatever customer of open a | |
| 00:11:19.600 --> 00:11:26.120 | |
| was like deploying a system and so in | |
| 00:11:22.760 --> 00:11:29.120 | |
| order to fix this basically what open AI | |
| 00:11:26.120 --> 00:11:30.480 | |
| did I believe I believe like they're | |
| 00:11:29.120 --> 00:11:32.279 | |
| don't actually tell you exactly what | |
| 00:11:30.480 --> 00:11:36.040 | |
| they did ever but I'm assuming what they | |
| 00:11:32.279 --> 00:11:37.680 | |
| did is they trained uh their models so | |
| 00:11:36.040 --> 00:11:39.240 | |
| that the models would not output | |
| 00:11:37.680 --> 00:11:41.639 | |
| anything that's included in the system | |
| 00:11:39.240 --> 00:11:43.839 | |
| message so the system message is used to | |
| 00:11:41.639 --> 00:11:46.120 | |
| influence behavior but it like they're | |
| 00:11:43.839 --> 00:11:48.200 | |
| explicitly trained to not output things | |
| 00:11:46.120 --> 00:11:49.880 | |
| that are included in there and so if you | |
| 00:11:48.200 --> 00:11:53.360 | |
| put the | |
| 00:11:49.880 --> 00:11:56.200 | |
| actual if you put the actual thing that | |
| 00:11:53.360 --> 00:11:59.639 | |
| you wanted to evaluate within the system | |
| 00:11:56.200 --> 00:12:01.839 | |
| message it might still predict | |
| 00:11:59.639 --> 00:12:04.839 | |
| the sentiment correctly but it won't | |
| 00:12:01.839 --> 00:12:06.920 | |
| repeat the the stuff that was in system | |
| 00:12:04.839 --> 00:12:09.920 | |
| message | |
| 00:12:06.920 --> 00:12:09.920 | |
| B | |
| 00:12:14.160 --> 00:12:20.480 | |
| yeah after we give it the yeah yeah so | |
| 00:12:18.320 --> 00:12:23.040 | |
| the that's a great question so typically | |
| 00:12:20.480 --> 00:12:26.480 | |
| this is hand created so you you create | |
| 00:12:23.040 --> 00:12:29.680 | |
| something like this um I I have a a | |
| 00:12:26.480 --> 00:12:32.120 | |
| bracket X here but another way people | |
| 00:12:29.680 --> 00:12:33.800 | |
| typically specify this is you just have | |
| 00:12:32.120 --> 00:12:36.880 | |
| a | |
| 00:12:33.800 --> 00:12:41.199 | |
| big um you just have a big python string | |
| 00:12:36.880 --> 00:12:41.199 | |
| which is like um you know | |
| 00:12:42.040 --> 00:12:46.480 | |
| please um please | |
| 00:12:49.279 --> 00:12:55.440 | |
| specify and then you | |
| 00:12:52.440 --> 00:12:55.440 | |
| have | |
| 00:12:56.160 --> 00:13:02.240 | |
| um and then you substitute in uh like | |
| 00:12:59.880 --> 00:13:04.440 | |
| the input into this place here so you | |
| 00:13:02.240 --> 00:13:07.760 | |
| usually handw write it I'm going to | |
| 00:13:04.440 --> 00:13:07.760 | |
| talk excuse | |
| 00:13:07.800 --> 00:13:14.120 | |
| me and to end about some methods to | |
| 00:13:10.320 --> 00:13:16.120 | |
| learn these also but um I'd say like 90 | |
| 00:13:14.120 --> 00:13:18.320 | |
| 95% of the time people are just writing | |
| 00:13:16.120 --> 00:13:18.320 | |
| the | |
| 00:13:19.959 --> 00:13:24.560 | |
| man yep I would | |
| 00:13:25.920 --> 00:13:31.639 | |
| write | |
| 00:13:27.760 --> 00:13:31.639 | |
| and real input that | |
| 00:13:33.240 --> 00:13:38.040 | |
| I yeah so typically the template is | |
| 00:13:36.360 --> 00:13:39.800 | |
| written when you decide what system you | |
| 00:13:38.040 --> 00:13:41.839 | |
| want to create so you decide you want to | |
| 00:13:39.800 --> 00:13:44.519 | |
| create a sentiment analysis system so | |
| 00:13:41.839 --> 00:13:46.760 | |
| you create a template that either says | |
| 00:13:44.519 --> 00:13:48.079 | |
| like please classify the topic in the | |
| 00:13:46.760 --> 00:13:50.959 | |
| case of a model that was trained to | |
| 00:13:48.079 --> 00:13:52.240 | |
| follow instructions or if you have a | |
| 00:13:50.959 --> 00:13:54.240 | |
| base model that was not trained to | |
| 00:13:52.240 --> 00:13:58.079 | |
| follow instructions which is rare rare | |
| 00:13:54.240 --> 00:14:00.279 | |
| nowadays but gpd2 or La llama 2 without | |
| 00:13:58.079 --> 00:14:02.320 | |
| chat tuning is as an example of that | |
| 00:14:00.279 --> 00:14:05.600 | |
| then you would need to create a template | |
| 00:14:02.320 --> 00:14:10.040 | |
| that looks like this um where | |
| 00:14:05.600 --> 00:14:11.360 | |
| you put the model in a situation where | |
| 00:14:10.040 --> 00:14:13.839 | |
| the | |
| 00:14:11.360 --> 00:14:15.240 | |
| next word that follows up should be | |
| 00:14:13.839 --> 00:14:17.120 | |
| indicative of the answer to your | |
| 00:14:15.240 --> 00:14:20.120 | |
| question so like positive or negative or | |
| 00:14:17.120 --> 00:14:21.800 | |
| something like that so um but either way | |
| 00:14:20.120 --> 00:14:24.639 | |
| like usually you handw write this when | |
| 00:14:21.800 --> 00:14:27.199 | |
| you decide what task is you want to do | |
| 00:14:24.639 --> 00:14:29.000 | |
| then this input X this comes at test | |
| 00:14:27.199 --> 00:14:32.920 | |
| time this comes when you actually Dey | |
| 00:14:29.000 --> 00:14:34.240 | |
| your system um so this would be like an | |
| 00:14:32.920 --> 00:14:37.040 | |
| Amazon review that you wanted to | |
| 00:14:34.240 --> 00:14:37.040 | |
| classify using an | |
| 00:14:37.720 --> 00:14:42.720 | |
| image cool any other | |
| 00:14:40.519 --> 00:14:46.480 | |
| questions okay let's | |
| 00:14:42.720 --> 00:14:48.160 | |
| move um so basically this is what is | |
| 00:14:46.480 --> 00:14:49.920 | |
| happening behind the scenes I don't know | |
| 00:14:48.160 --> 00:14:53.040 | |
| what open AI format is because they | |
| 00:14:49.920 --> 00:14:54.639 | |
| won't tell us of course um but you know | |
| 00:14:53.040 --> 00:14:56.000 | |
| I'm assuming that that's similar to | |
| 00:14:54.639 --> 00:14:59.399 | |
| what's happening in | |
| 00:14:56.000 --> 00:15:01.959 | |
| op okay um so the next thing that we do | |
| 00:14:59.399 --> 00:15:05.360 | |
| is answer prediction so given uh The | |
| 00:15:01.959 --> 00:15:08.320 | |
| Prompt we predict the answer um and so | |
| 00:15:05.360 --> 00:15:11.880 | |
| using whatever algorithm we want to use | |
| 00:15:08.320 --> 00:15:14.880 | |
| uh we predict you know fantastic | |
| 00:15:11.880 --> 00:15:14.880 | |
| here | |
| 00:15:15.120 --> 00:15:21.639 | |
| um and actually it might not predict | |
| 00:15:19.959 --> 00:15:26.399 | |
| fantastic it might predict something | |
| 00:15:21.639 --> 00:15:28.120 | |
| else like overall it was um a really | |
| 00:15:26.399 --> 00:15:30.000 | |
| fantastic movie that I liked a lot or | |
| 00:15:28.120 --> 00:15:33.839 | |
| something like so it might also do | |
| 00:15:30.000 --> 00:15:36.880 | |
| something like that so based on that we | |
| 00:15:33.839 --> 00:15:39.600 | |
| want to select the actual output out of | |
| 00:15:36.880 --> 00:15:41.160 | |
| the generated uh outputs and I'm calling | |
| 00:15:39.600 --> 00:15:43.639 | |
| this uh | |
| 00:15:41.160 --> 00:15:45.959 | |
| postprocessing so for instance we might | |
| 00:15:43.639 --> 00:15:48.240 | |
| take the output as is so for something | |
| 00:15:45.959 --> 00:15:50.880 | |
| like just you interacting with chat | |
| 00:15:48.240 --> 00:15:53.360 | |
| jpt um or interacting with a chat model | |
| 00:15:50.880 --> 00:15:55.639 | |
| you might be looking at the text as is | |
| 00:15:53.360 --> 00:15:58.319 | |
| or it might be formatting the output for | |
| 00:15:55.639 --> 00:16:00.079 | |
| easy Vis visualization selecting only | |
| 00:15:58.319 --> 00:16:02.440 | |
| parts of the output that you want to use | |
| 00:16:00.079 --> 00:16:04.560 | |
| or mapping the output to other | |
| 00:16:02.440 --> 00:16:07.600 | |
| actions so to give an example of | |
| 00:16:04.560 --> 00:16:10.079 | |
| formatting this is a feature of uh chat | |
| 00:16:07.600 --> 00:16:13.440 | |
| GPT or Bard or any that you interact | |
| 00:16:10.079 --> 00:16:14.920 | |
| with but um I wrote please write a table | |
| 00:16:13.440 --> 00:16:18.759 | |
| with the last five presidents and their | |
| 00:16:14.920 --> 00:16:20.319 | |
| birth dates and chat GPT is happy to do | |
| 00:16:18.759 --> 00:16:22.000 | |
| this for me it says here is a table with | |
| 00:16:20.319 --> 00:16:24.920 | |
| the last five US presidents and their | |
| 00:16:22.000 --> 00:16:27.639 | |
| birth dates um Joe Biden Donald Trump | |
| 00:16:24.920 --> 00:16:31.720 | |
| Barack Obama George W wish Bill Clinton | |
| 00:16:27.639 --> 00:16:33.600 | |
| um but this is written in markdown um or | |
| 00:16:31.720 --> 00:16:35.079 | |
| I assume it's written in markdown so it | |
| 00:16:33.600 --> 00:16:37.880 | |
| basically makes this table and then | |
| 00:16:35.079 --> 00:16:39.319 | |
| renders it in an easy to view way so | |
| 00:16:37.880 --> 00:16:41.000 | |
| this is really important if you're | |
| 00:16:39.319 --> 00:16:42.440 | |
| building a user facing system because | |
| 00:16:41.000 --> 00:16:44.279 | |
| you want to be able to render these | |
| 00:16:42.440 --> 00:16:46.279 | |
| things but the only thing a large | |
| 00:16:44.279 --> 00:16:48.880 | |
| language model can output is text right | |
| 00:16:46.279 --> 00:16:50.279 | |
| it can output a string of tokens so uh | |
| 00:16:48.880 --> 00:16:54.000 | |
| this is a really good way to interact | |
| 00:16:50.279 --> 00:16:55.759 | |
| with it um I I followed by saying output | |
| 00:16:54.000 --> 00:16:58.720 | |
| that in Json format so it says here's | |
| 00:16:55.759 --> 00:17:00.360 | |
| the information in Json format and | |
| 00:16:58.720 --> 00:17:02.000 | |
| instead of just giving me a big Json | |
| 00:17:00.360 --> 00:17:04.199 | |
| string it gives me syntax highlighting | |
| 00:17:02.000 --> 00:17:06.880 | |
| and all the other stuff like this um | |
| 00:17:04.199 --> 00:17:09.760 | |
| presumably what it's doing here is it's | |
| 00:17:06.880 --> 00:17:12.839 | |
| outputting um like a triple hash or | |
| 00:17:09.760 --> 00:17:15.160 | |
| something like this um the reason why I | |
| 00:17:12.839 --> 00:17:17.600 | |
| know that is because | |
| 00:17:15.160 --> 00:17:21.079 | |
| like seems to be making a mistake down | |
| 00:17:17.600 --> 00:17:23.280 | |
| here for some reason um like uh | |
| 00:17:21.079 --> 00:17:25.079 | |
| outputting a weird Le formatted thing at | |
| 00:17:23.280 --> 00:17:26.160 | |
| that and so even chat GPT makes mistakes | |
| 00:17:25.079 --> 00:17:30.320 | |
| some of the | |
| 00:17:26.160 --> 00:17:32.400 | |
| time um | |
| 00:17:30.320 --> 00:17:33.960 | |
| cool um another thing that you might | |
| 00:17:32.400 --> 00:17:35.520 | |
| want to do is especially if you're not | |
| 00:17:33.960 --> 00:17:37.360 | |
| using it in like a a directly | |
| 00:17:35.520 --> 00:17:40.200 | |
| user-facing application but you want to | |
| 00:17:37.360 --> 00:17:41.840 | |
| use it to extract some information or | |
| 00:17:40.200 --> 00:17:45.440 | |
| make some classification decision or | |
| 00:17:41.840 --> 00:17:47.280 | |
| something like that um you often select | |
| 00:17:45.440 --> 00:17:49.880 | |
| information that's indicative of the | |
| 00:17:47.280 --> 00:17:52.360 | |
| answer and so I love this movie overall | |
| 00:17:49.880 --> 00:17:53.960 | |
| it was a movie that was simply fantastic | |
| 00:17:52.360 --> 00:17:56.600 | |
| um you can do things like extract | |
| 00:17:53.960 --> 00:17:59.440 | |
| keywords like fantastic and use that to | |
| 00:17:56.600 --> 00:18:01.360 | |
| indicate positive sentiment | |
| 00:17:59.440 --> 00:18:04.080 | |
| there's various methods for doing this | |
| 00:18:01.360 --> 00:18:05.919 | |
| and these are also used in the | |
| 00:18:04.080 --> 00:18:08.679 | |
| benchmarks that are used to evaluate | |
| 00:18:05.919 --> 00:18:09.799 | |
| language models so it's you know like | |
| 00:18:08.679 --> 00:18:11.039 | |
| even if you're not building an | |
| 00:18:09.799 --> 00:18:12.679 | |
| application directly but you're just | |
| 00:18:11.039 --> 00:18:14.120 | |
| trying to do well in this class and get | |
| 00:18:12.679 --> 00:18:15.679 | |
| like a high score on a leaderboard or | |
| 00:18:14.120 --> 00:18:20.320 | |
| something it's still useful to know | |
| 00:18:15.679 --> 00:18:22.159 | |
| about these things so um for things like | |
| 00:18:20.320 --> 00:18:24.039 | |
| classification um you can identify | |
| 00:18:22.159 --> 00:18:27.159 | |
| keywords like fantastic that might be | |
| 00:18:24.039 --> 00:18:29.120 | |
| indicative of the class another thing | |
| 00:18:27.159 --> 00:18:31.559 | |
| that's uh pretty common is for | |
| 00:18:29.120 --> 00:18:34.480 | |
| regression or numerical problems you | |
| 00:18:31.559 --> 00:18:37.440 | |
| identify numbers and pull out the | |
| 00:18:34.480 --> 00:18:40.400 | |
| numbers and use those numbers as the | |
| 00:18:37.440 --> 00:18:42.360 | |
| answer um for code uh you can pull out | |
| 00:18:40.400 --> 00:18:45.080 | |
| code Snippets and triple back ticks and | |
| 00:18:42.360 --> 00:18:46.960 | |
| then execute the code for example so all | |
| 00:18:45.080 --> 00:18:48.600 | |
| of these things are basically heuristic | |
| 00:18:46.960 --> 00:18:50.159 | |
| methods but they can be used to pull out | |
| 00:18:48.600 --> 00:18:53.440 | |
| the actual answer that you want from the | |
| 00:18:50.159 --> 00:18:53.440 | |
| text that's generated di | |
| 00:18:54.480 --> 00:19:00.320 | |
| know cool uh any questions about that | |
| 00:19:02.280 --> 00:19:07.880 | |
| the final thing is output mapping um | |
| 00:19:04.640 --> 00:19:11.120 | |
| given an answer uh map it into a class | |
| 00:19:07.880 --> 00:19:13.360 | |
| label or a continuous value and so this | |
| 00:19:11.120 --> 00:19:16.000 | |
| is doing something like taking fantastic | |
| 00:19:13.360 --> 00:19:18.480 | |
| and mapping it into the class | |
| 00:19:16.000 --> 00:19:21.000 | |
| positive uh and so you know if we want | |
| 00:19:18.480 --> 00:19:23.000 | |
| to extract fi one to five star ratings | |
| 00:19:21.000 --> 00:19:25.559 | |
| from reviews this is something you would | |
| 00:19:23.000 --> 00:19:29.360 | |
| need to do and very often it's like a | |
| 00:19:25.559 --> 00:19:33.880 | |
| one to um one class to | |
| 00:19:29.360 --> 00:19:35.720 | |
| many um many word mapping and uh by | |
| 00:19:33.880 --> 00:19:37.400 | |
| doing this you can basically get a more | |
| 00:19:35.720 --> 00:19:38.720 | |
| robust mapping onto the number that you | |
| 00:19:37.400 --> 00:19:42.400 | |
| actually | |
| 00:19:38.720 --> 00:19:42.400 | |
| want I actually | |
| 00:19:42.720 --> 00:19:48.919 | |
| coincidentally on uh on Twitter saw a | |
| 00:19:45.280 --> 00:19:48.919 | |
| really good example of this like a week | |
| 00:19:55.880 --> 00:20:00.520 | |
| ago and yeah I don't know if I'm going | |
| 00:19:59.120 --> 00:20:05.440 | |
| to be able to find it in a reasonable | |
| 00:20:00.520 --> 00:20:08.520 | |
| time frame but basically um there was | |
| 00:20:05.440 --> 00:20:11.080 | |
| a person who was using gp4 to create a | |
| 00:20:08.520 --> 00:20:14.120 | |
| model uh to like reward open source | |
| 00:20:11.080 --> 00:20:15.880 | |
| models for good and bad you know | |
| 00:20:14.120 --> 00:20:18.320 | |
| responses | |
| 00:20:15.880 --> 00:20:20.799 | |
| and they started out with giving it a | |
| 00:20:18.320 --> 00:20:24.480 | |
| one to five star rating and then they | |
| 00:20:20.799 --> 00:20:28.360 | |
| switched it into very good good okay bad | |
| 00:20:24.480 --> 00:20:31.280 | |
| very bad and then um then asked to | |
| 00:20:28.360 --> 00:20:34.520 | |
| generate you know those like very good | |
| 00:20:31.280 --> 00:20:37.039 | |
| good bad okay bad very bad instead of | |
| 00:20:34.520 --> 00:20:40.360 | |
| one to five and that worked a lot better | |
| 00:20:37.039 --> 00:20:43.480 | |
| like the GPT model was a lot more uh | |
| 00:20:40.360 --> 00:20:46.039 | |
| like likely to get the answer correct um | |
| 00:20:43.480 --> 00:20:48.880 | |
| than it was if you gave a one to five | |
| 00:20:46.039 --> 00:20:50.799 | |
| star rating so this is something you | |
| 00:20:48.880 --> 00:20:54.280 | |
| should think about pretty seriously and | |
| 00:20:50.799 --> 00:20:57.440 | |
| the way you can think about it is How | |
| 00:20:54.280 --> 00:20:59.679 | |
| likely was this data to appear in a | |
| 00:20:57.440 --> 00:21:02.520 | |
| large Corp of data on the | |
| 00:20:59.679 --> 00:21:04.760 | |
| internet and it might be like a lot less | |
| 00:21:02.520 --> 00:21:08.679 | |
| likely that it's like how good is this | |
| 00:21:04.760 --> 00:21:11.400 | |
| movie five then how good is this movie | |
| 00:21:08.679 --> 00:21:13.960 | |
| really good like just think of like the | |
| 00:21:11.400 --> 00:21:16.200 | |
| occurrence probability and you can even | |
| 00:21:13.960 --> 00:21:18.600 | |
| um like mine this data from the the web | |
| 00:21:16.200 --> 00:21:21.320 | |
| if you want to to try to find out the | |
| 00:21:18.600 --> 00:21:24.520 | |
| best you know | |
| 00:21:21.320 --> 00:21:30.039 | |
| like the best things | |
| 00:21:24.520 --> 00:21:30.039 | |
| there cool um any questions about this | |
| 00:21:35.360 --> 00:21:39.480 | |
| yeah how is | |
| 00:21:37.720 --> 00:21:43.039 | |
| it | |
| 00:21:39.480 --> 00:21:45.919 | |
| learning so the model the model is | |
| 00:21:43.039 --> 00:21:47.600 | |
| predicting txt and like accurately it's | |
| 00:21:45.919 --> 00:21:50.200 | |
| not even predicting the word fantastic | |
| 00:21:47.600 --> 00:21:54.480 | |
| it's predicting the token ID like | |
| 00:21:50.200 --> 00:21:57.600 | |
| 73521 or something like that um but you | |
| 00:21:54.480 --> 00:21:58.679 | |
| know if it has seen that token ID more | |
| 00:21:57.600 --> 00:22:00.840 | |
| frequent | |
| 00:21:58.679 --> 00:22:04.240 | |
| after reviews than it has seen the token | |
| 00:22:00.840 --> 00:22:06.000 | |
| ID for the number one or the number five | |
| 00:22:04.240 --> 00:22:07.520 | |
| then it's more likely to predict that | |
| 00:22:06.000 --> 00:22:10.279 | |
| accurately right it's more likely to | |
| 00:22:07.520 --> 00:22:11.880 | |
| predict fantastic than it is to predict | |
| 00:22:10.279 --> 00:22:14.679 | |
| five star or something like that just | |
| 00:22:11.880 --> 00:22:16.720 | |
| because fantastic is more frequent and | |
| 00:22:14.679 --> 00:22:18.880 | |
| so because of that if you think about | |
| 00:22:16.720 --> 00:22:22.120 | |
| like what has it seen in all of the data | |
| 00:22:18.880 --> 00:22:24.240 | |
| on the internet and like model your um | |
| 00:22:22.120 --> 00:22:26.960 | |
| model your answers here appropriately | |
| 00:22:24.240 --> 00:22:28.520 | |
| then that can give you | |
| 00:22:26.960 --> 00:22:30.320 | |
| betters | |
| 00:22:28.520 --> 00:22:32.120 | |
| this is a very important rule of thumb | |
| 00:22:30.320 --> 00:22:33.400 | |
| like don't try to make a language model | |
| 00:22:32.120 --> 00:22:35.039 | |
| do something it's never seen in the | |
| 00:22:33.400 --> 00:22:38.200 | |
| pre-training data and it will make your | |
| 00:22:35.039 --> 00:22:40.240 | |
| life a lot easier so um you can think | |
| 00:22:38.200 --> 00:22:41.880 | |
| that going forward | |
| 00:22:40.240 --> 00:22:44.679 | |
| to | |
| 00:22:41.880 --> 00:22:48.559 | |
| cool so next I want to move into fat | |
| 00:22:44.679 --> 00:22:49.679 | |
| prompting or in context learning um so | |
| 00:22:48.559 --> 00:22:52.159 | |
| fat | |
| 00:22:49.679 --> 00:22:54.440 | |
| prompting basically what we do is we | |
| 00:22:52.159 --> 00:22:55.799 | |
| provide a few examples of the task | |
| 00:22:54.440 --> 00:22:58.440 | |
| together with the | |
| 00:22:55.799 --> 00:23:00.080 | |
| instruction and the way this work works | |
| 00:22:58.440 --> 00:23:02.360 | |
| is you write an instruction like please | |
| 00:23:00.080 --> 00:23:05.919 | |
| classify movie reviews as positive or | |
| 00:23:02.360 --> 00:23:08.120 | |
| negative and add like input uh I really | |
| 00:23:05.919 --> 00:23:10.320 | |
| don't like this movie output negative uh | |
| 00:23:08.120 --> 00:23:12.480 | |
| input this movie is great output | |
| 00:23:10.320 --> 00:23:16.640 | |
| positive | |
| 00:23:12.480 --> 00:23:18.880 | |
| and this is um pretty effective the | |
| 00:23:16.640 --> 00:23:21.799 | |
| thing it's most effective for are | |
| 00:23:18.880 --> 00:23:24.400 | |
| twofold it's most effective for making | |
| 00:23:21.799 --> 00:23:26.360 | |
| sure that you get the formatting right | |
| 00:23:24.400 --> 00:23:27.640 | |
| uh because if you have a few examples | |
| 00:23:26.360 --> 00:23:28.679 | |
| the model will tend to follow those | |
| 00:23:27.640 --> 00:23:30.840 | |
| examples | |
| 00:23:28.679 --> 00:23:34.440 | |
| with respect to formatting especially if | |
| 00:23:30.840 --> 00:23:37.320 | |
| we're talking about like gp4 models um | |
| 00:23:34.440 --> 00:23:40.400 | |
| or strong GPT models it's also effective | |
| 00:23:37.320 --> 00:23:42.400 | |
| if you're using weaker models so like | |
| 00:23:40.400 --> 00:23:44.720 | |
| stronger models like gp4 tend to be | |
| 00:23:42.400 --> 00:23:46.720 | |
| pretty good at following instructions so | |
| 00:23:44.720 --> 00:23:49.520 | |
| if you say | |
| 00:23:46.720 --> 00:23:51.640 | |
| um please classify movie reviews as | |
| 00:23:49.520 --> 00:23:54.000 | |
| positive or negative it will be more | |
| 00:23:51.640 --> 00:23:56.279 | |
| likely to just output positive or | |
| 00:23:54.000 --> 00:23:58.760 | |
| negative um but if you have weaker | |
| 00:23:56.279 --> 00:24:01.720 | |
| models it might say I really don't like | |
| 00:23:58.760 --> 00:24:03.559 | |
| this movie output uh I think I think | |
| 00:24:01.720 --> 00:24:05.640 | |
| this is probably negative or something | |
| 00:24:03.559 --> 00:24:07.240 | |
| like that it will you know it might not | |
| 00:24:05.640 --> 00:24:10.080 | |
| follow the instructions as well and it's | |
| 00:24:07.240 --> 00:24:14.240 | |
| more effective to provide as in context | |
| 00:24:10.080 --> 00:24:17.600 | |
| examples um so so this is a one uh | |
| 00:24:14.240 --> 00:24:19.480 | |
| one uh thing to remember one thing I | |
| 00:24:17.600 --> 00:24:22.120 | |
| should mention also is when I say F shot | |
| 00:24:19.480 --> 00:24:25.720 | |
| prompting and in context learning these | |
| 00:24:22.120 --> 00:24:27.880 | |
| are basically the same thing uh they | |
| 00:24:25.720 --> 00:24:29.720 | |
| basically refer to the same concept but | |
| 00:24:27.880 --> 00:24:31.919 | |
| just from slightly different | |
| 00:24:29.720 --> 00:24:34.799 | |
| examples uh from sorry slightly | |
| 00:24:31.919 --> 00:24:36.919 | |
| different angles PE shot is in contrast | |
| 00:24:34.799 --> 00:24:39.320 | |
| to zero shot so zero shot means you're | |
| 00:24:36.919 --> 00:24:43.039 | |
| providing no examples so zero shot | |
| 00:24:39.320 --> 00:24:45.720 | |
| prompting you would have none uh few | |
| 00:24:43.039 --> 00:24:47.240 | |
| shot you have several examples in | |
| 00:24:45.720 --> 00:24:49.679 | |
| context learning means that you're | |
| 00:24:47.240 --> 00:24:51.640 | |
| learning how to do a task but instead of | |
| 00:24:49.679 --> 00:24:54.320 | |
| providing the model with fine-tuning | |
| 00:24:51.640 --> 00:24:56.679 | |
| data you're providing the examples in | |
| 00:24:54.320 --> 00:24:58.080 | |
| the language models context so they both | |
| 00:24:56.679 --> 00:25:00.919 | |
| basically mean the same thing but | |
| 00:24:58.080 --> 00:25:03.159 | |
| they're they're just contrasting to like | |
| 00:25:00.919 --> 00:25:06.559 | |
| either a zero shot or fine tuning which | |
| 00:25:03.159 --> 00:25:06.559 | |
| is why the terminology is | |
| 00:25:06.880 --> 00:25:13.520 | |
| different so they usering interface | |
| 00:25:11.320 --> 00:25:16.080 | |
| and for the | |
| 00:25:13.520 --> 00:25:17.760 | |
| rendering uh yes you can definitely do F | |
| 00:25:16.080 --> 00:25:20.039 | |
| shot prompting I'm actually going to | |
| 00:25:17.760 --> 00:25:23.440 | |
| talk exactly about exactly how you do | |
| 00:25:20.039 --> 00:25:26.320 | |
| this in like an open AI model um here | |
| 00:25:23.440 --> 00:25:28.240 | |
| which is for open AI models there's a | |
| 00:25:26.320 --> 00:25:31.320 | |
| couple ways that you could do this one | |
| 00:25:28.240 --> 00:25:33.640 | |
| way you could do this is you could um | |
| 00:25:31.320 --> 00:25:36.279 | |
| you could have the role be user and the | |
| 00:25:33.640 --> 00:25:39.279 | |
| role be assistant and just add like | |
| 00:25:36.279 --> 00:25:41.159 | |
| additional conversational history into | |
| 00:25:39.279 --> 00:25:43.159 | |
| the the messages that you're sending to | |
| 00:25:41.159 --> 00:25:46.240 | |
| the language model but actually the | |
| 00:25:43.159 --> 00:25:49.120 | |
| recommended way of doing this um which | |
| 00:25:46.240 --> 00:25:51.880 | |
| is in the openi cookbook uh which is in | |
| 00:25:49.120 --> 00:25:53.919 | |
| the reference is that you send this as a | |
| 00:25:51.880 --> 00:25:58.200 | |
| system message but you provide this like | |
| 00:25:53.919 --> 00:26:00.840 | |
| additional name variable here um with | |
| 00:25:58.200 --> 00:26:02.840 | |
| example user and example assistant the | |
| 00:26:00.840 --> 00:26:06.200 | |
| main reason why you do this is just | |
| 00:26:02.840 --> 00:26:08.080 | |
| because if you don't um if you send it | |
| 00:26:06.200 --> 00:26:10.600 | |
| in as the like user and assistant the | |
| 00:26:08.080 --> 00:26:12.799 | |
| model might refer back to the few shot | |
| 00:26:10.600 --> 00:26:14.320 | |
| examples as something that happened | |
| 00:26:12.799 --> 00:26:15.760 | |
| previously in the conversation whereas | |
| 00:26:14.320 --> 00:26:18.200 | |
| if you send it in the system message | |
| 00:26:15.760 --> 00:26:19.799 | |
| it's guaranteed to not do that so I | |
| 00:26:18.200 --> 00:26:23.600 | |
| think it's like less of an accuracy | |
| 00:26:19.799 --> 00:26:26.360 | |
| thing it's more of a like it's more of a | |
| 00:26:23.600 --> 00:26:29.120 | |
| privacy prompt privacy thing uh than | |
| 00:26:26.360 --> 00:26:30.880 | |
| anything else so this is a recommended | |
| 00:26:29.120 --> 00:26:33.159 | |
| way of doing this on the other hand if | |
| 00:26:30.880 --> 00:26:34.600 | |
| you're using like an open source model | |
| 00:26:33.159 --> 00:26:36.600 | |
| uh you need to be careful because this | |
| 00:26:34.600 --> 00:26:38.279 | |
| name might not even be included in the | |
| 00:26:36.600 --> 00:26:40.080 | |
| prompt template like for example in the | |
| 00:26:38.279 --> 00:26:41.840 | |
| light llm prompt templates that I was | |
| 00:26:40.080 --> 00:26:44.080 | |
| sending in this is not even included at | |
| 00:26:41.840 --> 00:26:46.480 | |
| all so you might just get a weird system | |
| 00:26:44.080 --> 00:26:49.720 | |
| message that uh is poorly fored so you | |
| 00:26:46.480 --> 00:26:53.600 | |
| need to be a little bit conscious | |
| 00:26:49.720 --> 00:26:55.799 | |
| this um cool any questions here does | |
| 00:26:53.600 --> 00:26:58.880 | |
| that answer the | |
| 00:26:55.799 --> 00:27:02.279 | |
| question okay | |
| 00:26:58.880 --> 00:27:05.000 | |
| um so one one thing to be aware of is | |
| 00:27:02.279 --> 00:27:07.039 | |
| llms are sensitive to small changes and | |
| 00:27:05.000 --> 00:27:12.080 | |
| in context examples that you provide to | |
| 00:27:07.039 --> 00:27:14.600 | |
| them so uh previous work has examined | |
| 00:27:12.080 --> 00:27:19.399 | |
| this from a number of angles there's a | |
| 00:27:14.600 --> 00:27:22.679 | |
| paper by Luol and they examine the | |
| 00:27:19.399 --> 00:27:25.000 | |
| sensitivity to example ordering so like | |
| 00:27:22.679 --> 00:27:28.399 | |
| if you take the same examples and you | |
| 00:27:25.000 --> 00:27:30.840 | |
| just order them in different orders um | |
| 00:27:28.399 --> 00:27:32.679 | |
| you can actually get very wildly | |
| 00:27:30.840 --> 00:27:35.600 | |
| different | |
| 00:27:32.679 --> 00:27:37.520 | |
| results um and this is especially true | |
| 00:27:35.600 --> 00:27:40.320 | |
| for smaller models so the smaller models | |
| 00:27:37.520 --> 00:27:42.720 | |
| here are like the gpt2 models the larger | |
| 00:27:40.320 --> 00:27:47.440 | |
| models here are like the GPT the larger | |
| 00:27:42.720 --> 00:27:47.440 | |
| model here is GPT 3.5 uh I | |
| 00:27:48.399 --> 00:27:54.120 | |
| believe other things that people have | |
| 00:27:50.559 --> 00:27:56.760 | |
| looked at are label balance so um how | |
| 00:27:54.120 --> 00:27:58.559 | |
| important is it for the labels to be | |
| 00:27:56.760 --> 00:28:01.440 | |
| balanced | |
| 00:27:58.559 --> 00:28:02.799 | |
| um and if you're doing sentiment | |
| 00:28:01.440 --> 00:28:05.240 | |
| classification for example you might | |
| 00:28:02.799 --> 00:28:07.519 | |
| have only positive examples or only | |
| 00:28:05.240 --> 00:28:10.000 | |
| negative examples and if you have only | |
| 00:28:07.519 --> 00:28:13.279 | |
| positive or negative examples this can | |
| 00:28:10.000 --> 00:28:15.559 | |
| uh help or hurt your accuracy uh for | |
| 00:28:13.279 --> 00:28:17.200 | |
| example on this Amazon review data set | |
| 00:28:15.559 --> 00:28:18.679 | |
| most of the reviews are positive so you | |
| 00:28:17.200 --> 00:28:20.840 | |
| actually do better by having lots of | |
| 00:28:18.679 --> 00:28:23.640 | |
| positive examples in your in context | |
| 00:28:20.840 --> 00:28:26.600 | |
| examples on the other hand for sst2 this | |
| 00:28:23.640 --> 00:28:29.159 | |
| is label balanced so having only | |
| 00:28:26.600 --> 00:28:31.799 | |
| positive or negative is worse on average | |
| 00:28:29.159 --> 00:28:34.279 | |
| than having three positive and one | |
| 00:28:31.799 --> 00:28:36.679 | |
| negative another thing is label coverage | |
| 00:28:34.279 --> 00:28:38.679 | |
| so if we're talking about multi class | |
| 00:28:36.679 --> 00:28:41.120 | |
| classification um | |
| 00:28:38.679 --> 00:28:42.919 | |
| having good coverage of all of the | |
| 00:28:41.120 --> 00:28:45.919 | |
| classes that you want to include in your | |
| 00:28:42.919 --> 00:28:49.120 | |
| multiclass classification is important | |
| 00:28:45.919 --> 00:28:51.720 | |
| um to some extent but if you have uh | |
| 00:28:49.120 --> 00:28:53.440 | |
| more uh you can also confuse some model | |
| 00:28:51.720 --> 00:28:55.840 | |
| especially if they're minority labels so | |
| 00:28:53.440 --> 00:28:57.799 | |
| if you have a whole bunch of like random | |
| 00:28:55.840 --> 00:28:59.080 | |
| minority labels and that can cause so | |
| 00:28:57.799 --> 00:29:01.399 | |
| this is something important to think | |
| 00:28:59.080 --> 00:29:04.640 | |
| about if you're planning on solving kind | |
| 00:29:01.399 --> 00:29:08.640 | |
| of like classification tests um I I've | |
| 00:29:04.640 --> 00:29:11.000 | |
| also had my own experience with uh using | |
| 00:29:08.640 --> 00:29:13.159 | |
| GPT for evaluation for machine | |
| 00:29:11.000 --> 00:29:14.760 | |
| translation and when we use GPT for | |
| 00:29:13.159 --> 00:29:18.559 | |
| evaluation for machine translation it | |
| 00:29:14.760 --> 00:29:20.799 | |
| was very important to add um like high | |
| 00:29:18.559 --> 00:29:22.760 | |
| uh high scoring values low score high | |
| 00:29:20.799 --> 00:29:26.320 | |
| scoring outputs low scoring outputs some | |
| 00:29:22.760 --> 00:29:27.840 | |
| in the middle um and so it's also the | |
| 00:29:26.320 --> 00:29:30.760 | |
| case for regression | |
| 00:29:27.840 --> 00:29:30.760 | |
| uh problems as | |
| 00:29:32.600 --> 00:29:37.320 | |
| well cool um any questions | |
| 00:29:38.159 --> 00:29:45.000 | |
| here um however this is not super | |
| 00:29:42.240 --> 00:29:46.600 | |
| predictable um so there's not like any | |
| 00:29:45.000 --> 00:29:48.399 | |
| rule of thumb that tells you like this | |
| 00:29:46.600 --> 00:29:49.720 | |
| is or as far as I know there's not any | |
| 00:29:48.399 --> 00:29:51.640 | |
| rule of thumb that tells you this is the | |
| 00:29:49.720 --> 00:29:54.000 | |
| way you should construct in context | |
| 00:29:51.640 --> 00:29:55.880 | |
| examples uh there are lots of papers | |
| 00:29:54.000 --> 00:29:57.799 | |
| that say they have methods that work | |
| 00:29:55.880 --> 00:30:01.000 | |
| better but I don't know if there's any | |
| 00:29:57.799 --> 00:30:02.559 | |
| like gold standard IND indry practice | |
| 00:30:01.000 --> 00:30:05.799 | |
| for doing something like this at the | |
| 00:30:02.559 --> 00:30:07.799 | |
| moment so just to give an example uh | |
| 00:30:05.799 --> 00:30:10.399 | |
| this paper it's a really nice paper | |
| 00:30:07.799 --> 00:30:13.440 | |
| examining why uh in context Learning | |
| 00:30:10.399 --> 00:30:17.279 | |
| Works one thing one interesting finding | |
| 00:30:13.440 --> 00:30:19.760 | |
| that they have is they output they take | |
| 00:30:17.279 --> 00:30:22.720 | |
| in context examples but they randomize | |
| 00:30:19.760 --> 00:30:27.320 | |
| the labels they make the labels wrong | |
| 00:30:22.720 --> 00:30:29.519 | |
| some of the time so even with completely | |
| 00:30:27.320 --> 00:30:32.120 | |
| wrong labels even with labels that are | |
| 00:30:29.519 --> 00:30:34.399 | |
| correct 0% of the time you still get | |
| 00:30:32.120 --> 00:30:37.360 | |
| much much better accuracy than if you | |
| 00:30:34.399 --> 00:30:39.440 | |
| use no Inc context examples and why is | |
| 00:30:37.360 --> 00:30:41.640 | |
| this probably you know it's getting the | |
| 00:30:39.440 --> 00:30:44.600 | |
| model formatting correct it's getting | |
| 00:30:41.640 --> 00:30:47.679 | |
| like the names of the labels correct | |
| 00:30:44.600 --> 00:30:49.039 | |
| even if it's not uh accurate so it seems | |
| 00:30:47.679 --> 00:30:50.519 | |
| like it's not really using these for | |
| 00:30:49.039 --> 00:30:52.640 | |
| training data it's using them more just | |
| 00:30:50.519 --> 00:30:56.240 | |
| to know the formatting | |
| 00:30:52.640 --> 00:30:59.399 | |
| appropriate like | |
| 00:30:56.240 --> 00:31:01.399 | |
| you so you already | |
| 00:30:59.399 --> 00:31:03.760 | |
| have | |
| 00:31:01.399 --> 00:31:08.840 | |
| right how is it | |
| 00:31:03.760 --> 00:31:11.240 | |
| Ma like is it just y one y i gu I'm just | |
| 00:31:08.840 --> 00:31:15.000 | |
| ask how you would inter | |
| 00:31:11.240 --> 00:31:16.480 | |
| that so this is you're not training the | |
| 00:31:15.000 --> 00:31:17.880 | |
| model at the moment we're going to talk | |
| 00:31:16.480 --> 00:31:19.360 | |
| about that next class but right now | |
| 00:31:17.880 --> 00:31:21.279 | |
| you're taking a model that has already | |
| 00:31:19.360 --> 00:31:22.840 | |
| been trained and you're providing it | |
| 00:31:21.279 --> 00:31:25.519 | |
| with a few examples and then you're | |
| 00:31:22.840 --> 00:31:28.679 | |
| asking it to fill in um the following | |
| 00:31:25.519 --> 00:31:30.880 | |
| examples just examples | |
| 00:31:28.679 --> 00:31:32.960 | |
| yes | |
| 00:31:30.880 --> 00:31:34.679 | |
| exactly and it's pretty amazing that | |
| 00:31:32.960 --> 00:31:36.440 | |
| that works in the first place especially | |
| 00:31:34.679 --> 00:31:39.840 | |
| with a model that hasn't been explicitly | |
| 00:31:36.440 --> 00:31:41.200 | |
| trained that way but um there's a a fair | |
| 00:31:39.840 --> 00:31:42.320 | |
| amount of research that I think we're | |
| 00:31:41.200 --> 00:31:43.960 | |
| probably going to be talking about in | |
| 00:31:42.320 --> 00:31:47.000 | |
| the interpretability class about why | |
| 00:31:43.960 --> 00:31:49.600 | |
| this happens but um | |
| 00:31:47.000 --> 00:31:51.279 | |
| basically my my interpretation for why | |
| 00:31:49.600 --> 00:31:53.679 | |
| this happens is because there's so much | |
| 00:31:51.279 --> 00:31:56.000 | |
| repetitive stuff on the internet right | |
| 00:31:53.679 --> 00:31:58.240 | |
| there's a bunch of examples of math | |
| 00:31:56.000 --> 00:32:00.399 | |
| problems which is like | |
| 00:31:58.240 --> 00:32:02.279 | |
| question one and then the math problem | |
| 00:32:00.399 --> 00:32:04.320 | |
| and then the answer question two math | |
| 00:32:02.279 --> 00:32:06.440 | |
| problem and then the answer so in order | |
| 00:32:04.320 --> 00:32:08.320 | |
| to model the text on the internet it | |
| 00:32:06.440 --> 00:32:12.120 | |
| needs to learn how to be able to do | |
| 00:32:08.320 --> 00:32:15.399 | |
| these things but so um | |
| 00:32:12.120 --> 00:32:17.760 | |
| cool the second thing is uh more | |
| 00:32:15.399 --> 00:32:20.000 | |
| demonstrations can sometimes hurt | |
| 00:32:17.760 --> 00:32:22.120 | |
| accuracy so this is like binary | |
| 00:32:20.000 --> 00:32:25.080 | |
| classification versus multiple choice | |
| 00:32:22.120 --> 00:32:27.440 | |
| question answering um and actually with | |
| 00:32:25.080 --> 00:32:30.919 | |
| binary classification the model ends up | |
| 00:32:27.440 --> 00:32:33.159 | |
| getting worse um with uh more examples | |
| 00:32:30.919 --> 00:32:36.799 | |
| probably just because the longer context | |
| 00:32:33.159 --> 00:32:39.320 | |
| uh you know confuses the model or moves | |
| 00:32:36.799 --> 00:32:41.320 | |
| the instructions that are provided to | |
| 00:32:39.320 --> 00:32:44.279 | |
| the model farther away in the context so | |
| 00:32:41.320 --> 00:32:48.120 | |
| it starts forgetting them so | |
| 00:32:44.279 --> 00:32:50.240 | |
| um basically what I want to say is uh | |
| 00:32:48.120 --> 00:32:51.760 | |
| you know this is more of an art than a | |
| 00:32:50.240 --> 00:32:53.279 | |
| science you might not get entirely | |
| 00:32:51.760 --> 00:32:55.840 | |
| predictable results but don't worry it's | |
| 00:32:53.279 --> 00:32:59.320 | |
| not just | |
| 00:32:55.840 --> 00:32:59.320 | |
| you cool cool | |
| 00:33:09.200 --> 00:33:15.320 | |
| yeah it can't so the question is if the | |
| 00:33:12.639 --> 00:33:17.039 | |
| in context examples reflect the data | |
| 00:33:15.320 --> 00:33:18.919 | |
| distribution well would that boost the | |
| 00:33:17.039 --> 00:33:24.240 | |
| accuracy I think the answer is probably | |
| 00:33:18.919 --> 00:33:26.039 | |
| yes yeah um I don't know if that it's | |
| 00:33:24.240 --> 00:33:27.679 | |
| that clear because like what I would | |
| 00:33:26.039 --> 00:33:29.919 | |
| expect | |
| 00:33:27.679 --> 00:33:33.559 | |
| is better | |
| 00:33:29.919 --> 00:33:37.240 | |
| coverage is probably more | |
| 00:33:33.559 --> 00:33:39.760 | |
| important than better representativeness | |
| 00:33:37.240 --> 00:33:41.960 | |
| so like even if you have some minority | |
| 00:33:39.760 --> 00:33:43.639 | |
| labels um it's probably better for the | |
| 00:33:41.960 --> 00:33:44.880 | |
| model to know what those minority labels | |
| 00:33:43.639 --> 00:33:47.279 | |
| look like and that's going to be | |
| 00:33:44.880 --> 00:33:49.120 | |
| especially true for like stronger models | |
| 00:33:47.279 --> 00:33:50.679 | |
| um I think | |
| 00:33:49.120 --> 00:33:54.320 | |
| so | |
| 00:33:50.679 --> 00:33:56.440 | |
| cool okay so uh next I want to talk | |
| 00:33:54.320 --> 00:33:59.000 | |
| about Chain of Thought prompting um so | |
| 00:33:56.440 --> 00:34:01.320 | |
| Chain of Thought prompting is a very | |
| 00:33:59.000 --> 00:34:04.080 | |
| popular way of prompting | |
| 00:34:01.320 --> 00:34:06.080 | |
| models and the way it works is you get | |
| 00:34:04.080 --> 00:34:07.839 | |
| the model to explain its reasoning | |
| 00:34:06.080 --> 00:34:12.679 | |
| before making an | |
| 00:34:07.839 --> 00:34:14.520 | |
| answer um and so sorry this example is a | |
| 00:34:12.679 --> 00:34:18.879 | |
| little bit small but like the standard | |
| 00:34:14.520 --> 00:34:20.480 | |
| prompting method is uh like Roger has | |
| 00:34:18.879 --> 00:34:22.000 | |
| five tennis balls he buys two more cans | |
| 00:34:20.480 --> 00:34:23.480 | |
| of tennis balls each can has three | |
| 00:34:22.000 --> 00:34:28.200 | |
| tennis balls how many tennis balls does | |
| 00:34:23.480 --> 00:34:29.359 | |
| he have now um the answer is 11 and so | |
| 00:34:28.200 --> 00:34:32.119 | |
| um this | |
| 00:34:29.359 --> 00:34:34.320 | |
| is an in context example and then you | |
| 00:34:32.119 --> 00:34:37.240 | |
| have your input which has a different | |
| 00:34:34.320 --> 00:34:39.000 | |
| problem uh the cafeteria has 23 apples | |
| 00:34:37.240 --> 00:34:40.639 | |
| if they Ed 20 to make lunch and bought | |
| 00:34:39.000 --> 00:34:41.800 | |
| six more how many apples do they have | |
| 00:34:40.639 --> 00:34:46.720 | |
| the answer is | |
| 00:34:41.800 --> 00:34:49.000 | |
| 27 um and so this is wrong so what Chain | |
| 00:34:46.720 --> 00:34:52.000 | |
| of Thought prompting does is instead of | |
| 00:34:49.000 --> 00:34:54.960 | |
| just giving the answer it gives you an | |
| 00:34:52.000 --> 00:34:57.079 | |
| additional reasoning chain uh that says | |
| 00:34:54.960 --> 00:34:59.680 | |
| R started with five balls two cans of of | |
| 00:34:57.079 --> 00:35:01.800 | |
| three tennis balls uh each of six tennis | |
| 00:34:59.680 --> 00:35:04.520 | |
| balls 5 plus 6 equals 11 the answer is | |
| 00:35:01.800 --> 00:35:06.280 | |
| 11 and so then when you feed this in | |
| 00:35:04.520 --> 00:35:08.000 | |
| basically the model will generate a | |
| 00:35:06.280 --> 00:35:10.240 | |
| similar reasoning chain and then it's | |
| 00:35:08.000 --> 00:35:13.400 | |
| more likely to get the answer correct | |
| 00:35:10.240 --> 00:35:15.720 | |
| and this very robustly works | |
| 00:35:13.400 --> 00:35:19.440 | |
| for many | |
| 00:35:15.720 --> 00:35:21.440 | |
| different problems where a reasoning | |
| 00:35:19.440 --> 00:35:23.520 | |
| chain is | |
| 00:35:21.440 --> 00:35:27.839 | |
| necessary and if you think about the | |
| 00:35:23.520 --> 00:35:30.359 | |
| reason why this uh why this works I | |
| 00:35:27.839 --> 00:35:33.040 | |
| think there's basically two reasons why | |
| 00:35:30.359 --> 00:35:34.440 | |
| um the first reason is I I only wrote | |
| 00:35:33.040 --> 00:35:36.560 | |
| one on the thing here but the first | |
| 00:35:34.440 --> 00:35:38.760 | |
| reason is it allows the model to | |
| 00:35:36.560 --> 00:35:41.359 | |
| decompose harder problems into simpler | |
| 00:35:38.760 --> 00:35:45.119 | |
| problems and simpler problems are easier | |
| 00:35:41.359 --> 00:35:47.560 | |
| right so um instead | |
| 00:35:45.119 --> 00:35:51.319 | |
| of immediately trying to solve the whole | |
| 00:35:47.560 --> 00:35:53.800 | |
| problem in a single go it will first | |
| 00:35:51.319 --> 00:35:56.520 | |
| solve the problem of like what how many | |
| 00:35:53.800 --> 00:35:58.920 | |
| are left after you use buy and so it | |
| 00:35:56.520 --> 00:36:00.240 | |
| gets three and so now it has this three | |
| 00:35:58.920 --> 00:36:02.480 | |
| here so now it can solve the next | |
| 00:36:00.240 --> 00:36:05.160 | |
| problem of adding six that's equal to 9 | |
| 00:36:02.480 --> 00:36:07.880 | |
| so it's solving simpler sub problems | |
| 00:36:05.160 --> 00:36:11.440 | |
| than it is and uh compared to harder | |
| 00:36:07.880 --> 00:36:13.920 | |
| ones another reason why is it allows for | |
| 00:36:11.440 --> 00:36:17.319 | |
| adaptive computation time so if you | |
| 00:36:13.920 --> 00:36:17.319 | |
| think about like a Transformer | |
| 00:36:19.000 --> 00:36:23.119 | |
| model um if you think about a | |
| 00:36:21.280 --> 00:36:25.560 | |
| Transformer model a Transformer model | |
| 00:36:23.119 --> 00:36:27.200 | |
| has fixed computation time for | |
| 00:36:25.560 --> 00:36:29.920 | |
| predicting each token right a fixed | |
| 00:36:27.200 --> 00:36:31.560 | |
| number of layers it um and based on that | |
| 00:36:29.920 --> 00:36:33.839 | |
| fixed number of layers it passes all the | |
| 00:36:31.560 --> 00:36:36.520 | |
| information through and makes a | |
| 00:36:33.839 --> 00:36:38.200 | |
| prediction and some problems are harder | |
| 00:36:36.520 --> 00:36:39.599 | |
| than others right so it would be very | |
| 00:36:38.200 --> 00:36:42.480 | |
| wasteful to have a really big | |
| 00:36:39.599 --> 00:36:45.640 | |
| Transformer that could solve you know | |
| 00:36:42.480 --> 00:36:49.119 | |
| really complex math problems in the same | |
| 00:36:45.640 --> 00:36:53.359 | |
| amount of time it takes to predict that | |
| 00:36:49.119 --> 00:36:55.280 | |
| the next word is like uh dog after the | |
| 00:36:53.359 --> 00:36:57.280 | |
| word the big or something like that | |
| 00:36:55.280 --> 00:36:58.560 | |
| right so there are some things that are | |
| 00:36:57.280 --> 00:37:00.000 | |
| easy we can do in a second there are | |
| 00:36:58.560 --> 00:37:01.839 | |
| some things that take us more time and | |
| 00:37:00.000 --> 00:37:05.880 | |
| essentially this Chain of Thought | |
| 00:37:01.839 --> 00:37:09.280 | |
| reasoning is um is doing that it's | |
| 00:37:05.880 --> 00:37:12.280 | |
| giving it more time to solve the harder | |
| 00:37:09.280 --> 00:37:12.280 | |
| problems | |
| 00:37:17.200 --> 00:37:22.440 | |
| yes | |
| 00:37:18.839 --> 00:37:23.960 | |
| okay yeah good good question so so | |
| 00:37:22.440 --> 00:37:26.200 | |
| that's what um that's what this next | |
| 00:37:23.960 --> 00:37:27.920 | |
| paper does so uh the the question was | |
| 00:37:26.200 --> 00:37:31.160 | |
| what what happens if we just ask it to | |
| 00:37:27.920 --> 00:37:34.800 | |
| reason and the answer is it still works | |
| 00:37:31.160 --> 00:37:37.000 | |
| um and this paper was really like I I I | |
| 00:37:34.800 --> 00:37:39.760 | |
| love this paper for its Simplicity and | |
| 00:37:37.000 --> 00:37:43.160 | |
| cleverness and basically uh they | |
| 00:37:39.760 --> 00:37:45.000 | |
| contrast few shot learning few shot | |
| 00:37:43.160 --> 00:37:49.200 | |
| Chain of Thought where you provide Chain | |
| 00:37:45.000 --> 00:37:52.160 | |
| of Thought examples zero shot prompting | |
| 00:37:49.200 --> 00:37:54.560 | |
| basically and zero shot Chain of Thought | |
| 00:37:52.160 --> 00:37:58.720 | |
| So what they do is they just | |
| 00:37:54.560 --> 00:38:00.280 | |
| add uh let's thinks step by step that | |
| 00:37:58.720 --> 00:38:04.200 | |
| they add that phrase to the end of The | |
| 00:38:00.280 --> 00:38:06.079 | |
| Prompt and then that elicits the model | |
| 00:38:04.200 --> 00:38:08.000 | |
| to basically do Chain of Thought | |
| 00:38:06.079 --> 00:38:09.240 | |
| reasoning without any further examples | |
| 00:38:08.000 --> 00:38:12.599 | |
| of how that Chain of Thought reasoning | |
| 00:38:09.240 --> 00:38:14.440 | |
| works why does this work again because | |
| 00:38:12.599 --> 00:38:16.760 | |
| like on the internet there's a bunch of | |
| 00:38:14.440 --> 00:38:20.240 | |
| examples of math problem solving data | |
| 00:38:16.760 --> 00:38:22.800 | |
| sets or QA corpora where it says let | |
| 00:38:20.240 --> 00:38:24.480 | |
| things step by step and after that you | |
| 00:38:22.800 --> 00:38:28.040 | |
| you know consistently have this sort of | |
| 00:38:24.480 --> 00:38:29.800 | |
| resoning chain added there so um good | |
| 00:38:28.040 --> 00:38:31.200 | |
| good intuition uh that this paper | |
| 00:38:29.800 --> 00:38:32.480 | |
| answers the question and this like | |
| 00:38:31.200 --> 00:38:36.119 | |
| actually does | |
| 00:38:32.480 --> 00:38:39.119 | |
| work one interesting thing is | |
| 00:38:36.119 --> 00:38:39.119 | |
| um | |
| 00:38:39.720 --> 00:38:45.200 | |
| now if I go to chat | |
| 00:38:47.319 --> 00:38:52.240 | |
| GPT and I say | |
| 00:38:50.480 --> 00:38:58.520 | |
| um | |
| 00:38:52.240 --> 00:38:58.520 | |
| I am teaching a class with 98 | |
| 00:38:58.720 --> 00:39:06.000 | |
| students | |
| 00:39:01.480 --> 00:39:08.400 | |
| 70% turn in the | |
| 00:39:06.000 --> 00:39:10.720 | |
| assignment hint on | |
| 00:39:08.400 --> 00:39:17.720 | |
| time uh | |
| 00:39:10.720 --> 00:39:21.880 | |
| 10% and it in play how many did not ENT | |
| 00:39:17.720 --> 00:39:21.880 | |
| in let's see let's see if this | |
| 00:39:25.079 --> 00:39:29.200 | |
| works okay it's writing code for | |
| 00:39:29.440 --> 00:39:34.599 | |
| me which is that's a feature slide I | |
| 00:39:32.119 --> 00:39:37.319 | |
| kind of didn't uh I kind of didn't | |
| 00:39:34.599 --> 00:39:37.319 | |
| wanted to do | |
| 00:39:40.040 --> 00:39:44.160 | |
| that okay | |
| 00:39:45.920 --> 00:39:50.359 | |
| um I do not | |
| 00:39:54.280 --> 00:39:59.720 | |
| like okay so um | |
| 00:39:57.040 --> 00:39:59.720 | |
| it's a little bit | |
| 00:40:15.720 --> 00:40:21.839 | |
| slow okay so there there that worked but | |
| 00:40:19.680 --> 00:40:24.640 | |
| not that I did not say let's think step | |
| 00:40:21.839 --> 00:40:27.760 | |
| by step I didn't I didn't ask it to do | |
| 00:40:24.640 --> 00:40:29.240 | |
| this um and the reason why is um we're | |
| 00:40:27.760 --> 00:40:31.119 | |
| going to talk about instruction tuning | |
| 00:40:29.240 --> 00:40:34.359 | |
| next time but basically GPT has been | |
| 00:40:31.119 --> 00:40:36.560 | |
| tuned to do this reasoning even if you | |
| 00:40:34.359 --> 00:40:38.480 | |
| don't ask it to do that uh it wouldn't | |
| 00:40:36.560 --> 00:40:40.839 | |
| do that naturally but it's because lots | |
| 00:40:38.480 --> 00:40:43.880 | |
| of supervised data has been added into | |
| 00:40:40.839 --> 00:40:46.920 | |
| this model so like another thing is like | |
| 00:40:43.880 --> 00:40:48.960 | |
| if you are planning on doing anything | |
| 00:40:46.920 --> 00:40:51.240 | |
| about like Chain of Thought reasoning or | |
| 00:40:48.960 --> 00:40:53.000 | |
| or stuff like that as a class project | |
| 00:40:51.240 --> 00:40:54.960 | |
| you need to keep in mind that the like | |
| 00:40:53.000 --> 00:40:58.280 | |
| GPD models have already been trained to | |
| 00:40:54.960 --> 00:41:00.040 | |
| do this and so so if you want to like | |
| 00:40:58.280 --> 00:41:01.599 | |
| try to find out a better way to elicit | |
| 00:41:00.040 --> 00:41:03.960 | |
| this from a raw model you'll need to use | |
| 00:41:01.599 --> 00:41:07.119 | |
| a raw model like llama 2 with no chat | |
| 00:41:03.960 --> 00:41:10.200 | |
| tuning or stuff like that um in order to | |
| 00:41:07.119 --> 00:41:12.520 | |
| uh do that in a neutral in a neutral | |
| 00:41:10.200 --> 00:41:14.960 | |
| setting that hasn't been contaminated by | |
| 00:41:12.520 --> 00:41:14.960 | |
| like super | |
| 00:41:15.960 --> 00:41:20.520 | |
| L cool um any | |
| 00:41:21.079 --> 00:41:27.720 | |
| questions okay um so next I want to talk | |
| 00:41:24.720 --> 00:41:31.280 | |
| about prompting in programs that uh Chad | |
| 00:41:27.720 --> 00:41:35.560 | |
| GPD gave me a good example of uh why why | |
| 00:41:31.280 --> 00:41:37.160 | |
| this is useful or important um so | |
| 00:41:35.560 --> 00:41:40.640 | |
| there's two results actually both of | |
| 00:41:37.160 --> 00:41:43.440 | |
| these are are from my uh collaborators | |
| 00:41:40.640 --> 00:41:45.839 | |
| but the first one is um it demonstrates | |
| 00:41:43.440 --> 00:41:48.720 | |
| that structuring outputs at programs can | |
| 00:41:45.839 --> 00:41:51.599 | |
| help you get better results even if the | |
| 00:41:48.720 --> 00:41:55.119 | |
| task isn't a programmatic task so this | |
| 00:41:51.599 --> 00:41:57.000 | |
| is kind of interesting um so we were | |
| 00:41:55.119 --> 00:41:59.319 | |
| looking at predicting stru structured | |
| 00:41:57.000 --> 00:42:01.640 | |
| outputs and these structured outputs | |
| 00:41:59.319 --> 00:42:03.839 | |
| specifically are procedural knowledge | |
| 00:42:01.640 --> 00:42:06.920 | |
| like this so like how do we cook a pie | |
| 00:42:03.839 --> 00:42:09.040 | |
| or how do we serve pot pies on a plate | |
| 00:42:06.920 --> 00:42:10.800 | |
| and we had this procedural knowledge | |
| 00:42:09.040 --> 00:42:14.040 | |
| like take the pies out to pool open the | |
| 00:42:10.800 --> 00:42:16.079 | |
| cabinet drawer take out several plates | |
| 00:42:14.040 --> 00:42:17.720 | |
| and we wanted to know the dependencies | |
| 00:42:16.079 --> 00:42:19.520 | |
| between these so we could create a | |
| 00:42:17.720 --> 00:42:22.559 | |
| structured like procedural knowledge | |
| 00:42:19.520 --> 00:42:25.599 | |
| base so this is not an inherently code | |
| 00:42:22.559 --> 00:42:27.200 | |
| based task it's not a you know you could | |
| 00:42:25.599 --> 00:42:28.880 | |
| just ask | |
| 00:42:27.200 --> 00:42:32.160 | |
| the model in natural language and that | |
| 00:42:28.880 --> 00:42:35.000 | |
| would work as well so we structured | |
| 00:42:32.160 --> 00:42:37.720 | |
| things in a couple varieties so we had a | |
| 00:42:35.000 --> 00:42:39.800 | |
| textual format we had uh something in | |
| 00:42:37.720 --> 00:42:43.079 | |
| the dot format which is a way to draw | |
| 00:42:39.800 --> 00:42:45.480 | |
| graphs and then we had we also tried | |
| 00:42:43.079 --> 00:42:47.240 | |
| structuring the output in Python so | |
| 00:42:45.480 --> 00:42:48.960 | |
| these are just different ways to format | |
| 00:42:47.240 --> 00:42:50.720 | |
| the output they all say the same thing | |
| 00:42:48.960 --> 00:42:54.599 | |
| and we can extract the answer from all | |
| 00:42:50.720 --> 00:42:56.920 | |
| of them um but we found that structuring | |
| 00:42:54.599 --> 00:42:58.480 | |
| it in in Python basically is the more | |
| 00:42:56.920 --> 00:43:02.920 | |
| effective way of doing | |
| 00:42:58.480 --> 00:43:04.680 | |
| this so why why is it this the case the | |
| 00:43:02.920 --> 00:43:06.280 | |
| answer is essentially the same thing | |
| 00:43:04.680 --> 00:43:08.680 | |
| that I was talking about before with you | |
| 00:43:06.280 --> 00:43:11.480 | |
| know predicting excellent instead of | |
| 00:43:08.680 --> 00:43:13.319 | |
| five right you know it's seen a ton of | |
| 00:43:11.480 --> 00:43:15.960 | |
| python in it's training data so it's | |
| 00:43:13.319 --> 00:43:17.760 | |
| very good at predicting python uh it's | |
| 00:43:15.960 --> 00:43:20.359 | |
| less good at predicting dot format | |
| 00:43:17.760 --> 00:43:24.240 | |
| because it seemed less do format and it | |
| 00:43:20.359 --> 00:43:26.640 | |
| hasn't seen very much text here | |
| 00:43:24.240 --> 00:43:29.960 | |
| um another | |
| 00:43:26.640 --> 00:43:32.359 | |
| comment is code is very highly | |
| 00:43:29.960 --> 00:43:33.559 | |
| structured compared to natural language | |
| 00:43:32.359 --> 00:43:35.599 | |
| and because code is very highly | |
| 00:43:33.559 --> 00:43:37.520 | |
| structured we have things like | |
| 00:43:35.599 --> 00:43:39.079 | |
| dependencies where we refer back to | |
| 00:43:37.520 --> 00:43:41.079 | |
| variables that we defined before and | |
| 00:43:39.079 --> 00:43:44.119 | |
| other things like this so I think when | |
| 00:43:41.079 --> 00:43:46.760 | |
| it starts outputting code the models get | |
| 00:43:44.119 --> 00:43:48.359 | |
| into this mode which say yes please | |
| 00:43:46.760 --> 00:43:51.280 | |
| refer back to the things you've seen | |
| 00:43:48.359 --> 00:43:53.440 | |
| previously more often like attend to | |
| 00:43:51.280 --> 00:43:57.040 | |
| previous stuff more often and don't just | |
| 00:43:53.440 --> 00:43:59.760 | |
| like generate things you know uh | |
| 00:43:57.040 --> 00:44:02.440 | |
| arbitrarily and hallucinate you know new | |
| 00:43:59.760 --> 00:44:04.119 | |
| content and because of this for | |
| 00:44:02.440 --> 00:44:05.559 | |
| generating structured outputs even if | |
| 00:44:04.119 --> 00:44:08.920 | |
| the structured outputs don't need to be | |
| 00:44:05.559 --> 00:44:11.520 | |
| code you can benefit by doing | |
| 00:44:08.920 --> 00:44:13.200 | |
| this another thing that's a really handy | |
| 00:44:11.520 --> 00:44:16.319 | |
| trick is anytime you want to get a | |
| 00:44:13.200 --> 00:44:19.079 | |
| structured output out of a model | |
| 00:44:16.319 --> 00:44:22.760 | |
| um you can ask it to generate something | |
| 00:44:19.079 --> 00:44:24.839 | |
| in Json instead of generating it in uh | |
| 00:44:22.760 --> 00:44:26.640 | |
| in text and the reason why Json is | |
| 00:44:24.839 --> 00:44:28.079 | |
| useful is you can press the on you can | |
| 00:44:26.640 --> 00:44:32.319 | |
| pull out the strings and other stuff | |
| 00:44:28.079 --> 00:44:34.839 | |
| like that um so this can be very | |
| 00:44:32.319 --> 00:44:37.960 | |
| effective because if you just add an | |
| 00:44:34.839 --> 00:44:40.200 | |
| instruction that says please um please | |
| 00:44:37.960 --> 00:44:42.440 | |
| format things in this particular | |
| 00:44:40.200 --> 00:44:43.680 | |
| format often the model won't listen to | |
| 00:44:42.440 --> 00:44:44.800 | |
| you and it will output something in a | |
| 00:44:43.680 --> 00:44:46.280 | |
| different format you need to write a | |
| 00:44:44.800 --> 00:44:48.599 | |
| really annoying parser to pull out the | |
| 00:44:46.280 --> 00:44:50.280 | |
| information that you actually want but | |
| 00:44:48.599 --> 00:44:51.960 | |
| it gets Json right almost all of the | |
| 00:44:50.280 --> 00:44:54.040 | |
| time just because it's seen so much Json | |
| 00:44:51.960 --> 00:44:57.880 | |
| so that's a nice trick if you want to do | |
| 00:44:54.040 --> 00:45:01.520 | |
| something like that | |
| 00:44:57.880 --> 00:45:03.559 | |
| another uh thing is a paper uh called | |
| 00:45:01.520 --> 00:45:08.079 | |
| program AED language models that we did | |
| 00:45:03.559 --> 00:45:10.200 | |
| about a year ago and the method that we | |
| 00:45:08.079 --> 00:45:13.760 | |
| proposed here is using a program to | |
| 00:45:10.200 --> 00:45:16.480 | |
| generate outputs uh using a program to | |
| 00:45:13.760 --> 00:45:19.440 | |
| generate outputs and this can be more | |
| 00:45:16.480 --> 00:45:22.319 | |
| precise than asking an LM to do so and | |
| 00:45:19.440 --> 00:45:26.720 | |
| so instead of doing Chain of Thought | |
| 00:45:22.319 --> 00:45:30.640 | |
| prompting we created a few F shot | |
| 00:45:26.720 --> 00:45:34.319 | |
| examples where we wrote like the text | |
| 00:45:30.640 --> 00:45:37.160 | |
| here and then the text in English and | |
| 00:45:34.319 --> 00:45:40.160 | |
| then we had code corresponding code the | |
| 00:45:37.160 --> 00:45:42.280 | |
| text in English corresponding code and | |
| 00:45:40.160 --> 00:45:44.960 | |
| then the answer is and then the final | |
| 00:45:42.280 --> 00:45:48.160 | |
| code and then we basically generate this | |
| 00:45:44.960 --> 00:45:49.640 | |
| code and execute it to get the answer so | |
| 00:45:48.160 --> 00:45:52.319 | |
| like as you saw this is implemented in | |
| 00:45:49.640 --> 00:45:54.280 | |
| chat GP now it's uh you write something | |
| 00:45:52.319 --> 00:45:56.319 | |
| out it will decide whether it wants to | |
| 00:45:54.280 --> 00:45:58.599 | |
| generate code or generate text depending | |
| 00:45:56.319 --> 00:46:00.760 | |
| on the type of problem and it's just | |
| 00:45:58.599 --> 00:46:03.559 | |
| more precise it can solve like actually | |
| 00:46:00.760 --> 00:46:05.200 | |
| rather complex problems like uh you know | |
| 00:46:03.559 --> 00:46:07.880 | |
| calculating how much tax you need to be | |
| 00:46:05.200 --> 00:46:10.880 | |
| paying or something like that | |
| 00:46:07.880 --> 00:46:12.480 | |
| um it's especially useful for numeric | |
| 00:46:10.880 --> 00:46:14.599 | |
| questions and it's implemented in things | |
| 00:46:12.480 --> 00:46:17.040 | |
| like the chat GPT uh code interpreter | |
| 00:46:14.599 --> 00:46:18.640 | |
| bar to execution other things like that | |
| 00:46:17.040 --> 00:46:22.079 | |
| it's pretty cool it can actually do | |
| 00:46:18.640 --> 00:46:24.440 | |
| visualizations for you for papers also | |
| 00:46:22.079 --> 00:46:28.000 | |
| so if you ask it to visualize data for | |
| 00:46:24.440 --> 00:46:30.200 | |
| you um chat GPD now does a pretty good | |
| 00:46:28.000 --> 00:46:32.640 | |
| job of doing this like to give an | |
| 00:46:30.200 --> 00:46:34.319 | |
| example I asked it I gave it a big | |
| 00:46:32.640 --> 00:46:35.760 | |
| python list and asked it to generate a | |
| 00:46:34.319 --> 00:46:37.839 | |
| histogram and it did a really good job | |
| 00:46:35.760 --> 00:46:40.240 | |
| of it for me it also gives you the code | |
| 00:46:37.839 --> 00:46:42.839 | |
| so you can go in and modify it later so | |
| 00:46:40.240 --> 00:46:44.720 | |
| um I would definitely recommend you know | |
| 00:46:42.839 --> 00:46:46.200 | |
| thinking about using this uh either in | |
| 00:46:44.720 --> 00:46:49.200 | |
| your research or just to write your | |
| 00:46:46.200 --> 00:46:51.480 | |
| reports uh for this class so um this | |
| 00:46:49.200 --> 00:46:55.839 | |
| class is uh generative AI friendly | |
| 00:46:51.480 --> 00:46:57.760 | |
| mostly so like I do I do want you to | |
| 00:46:55.839 --> 00:46:59.880 | |
| learn the things we expect you to learn | |
| 00:46:57.760 --> 00:47:02.480 | |
| which is why I suggest that you don't | |
| 00:46:59.880 --> 00:47:04.400 | |
| like just write every uh everything for | |
| 00:47:02.480 --> 00:47:06.280 | |
| assignment number one with chat GP key | |
| 00:47:04.400 --> 00:47:07.720 | |
| but I think even if you tried to do that | |
| 00:47:06.280 --> 00:47:09.640 | |
| it'd probably get it wrong in subtle | |
| 00:47:07.720 --> 00:47:10.920 | |
| ways so you're probably better off | |
| 00:47:09.640 --> 00:47:13.880 | |
| understanding the content | |
| 00:47:10.920 --> 00:47:16.400 | |
| anyway um | |
| 00:47:13.880 --> 00:47:18.160 | |
| cool this can also be expanded a whole | |
| 00:47:16.400 --> 00:47:21.559 | |
| lot into like agents and tools and I'm | |
| 00:47:18.160 --> 00:47:21.559 | |
| going to talk about that separately | |
| 00:47:22.800 --> 00:47:27.720 | |
| later cool uh any any things about this | |
| 00:47:29.040 --> 00:47:34.200 | |
| okay I'm uh I'm going to go next so | |
| 00:47:31.800 --> 00:47:36.079 | |
| prompt engineering um when you're | |
| 00:47:34.200 --> 00:47:37.280 | |
| designing prompts uh there's a number of | |
| 00:47:36.079 --> 00:47:38.240 | |
| different ways you can do this you can | |
| 00:47:37.280 --> 00:47:41.559 | |
| do this | |
| 00:47:38.240 --> 00:47:42.960 | |
| manually uh you to do this you configure | |
| 00:47:41.559 --> 00:47:44.520 | |
| a manual template based on the | |
| 00:47:42.960 --> 00:47:46.160 | |
| characteristics of the task using all of | |
| 00:47:44.520 --> 00:47:48.880 | |
| the knowledge that I told you | |
| 00:47:46.160 --> 00:47:50.119 | |
| before you can also do automated search | |
| 00:47:48.880 --> 00:47:52.079 | |
| and there's a number of different ways | |
| 00:47:50.119 --> 00:47:55.119 | |
| to do automated search for | |
| 00:47:52.079 --> 00:47:58.319 | |
| prompts uh the first one is doing some | |
| 00:47:55.119 --> 00:48:00.599 | |
| sort of search discret space uh so you | |
| 00:47:58.319 --> 00:48:02.720 | |
| find a prompt that is | |
| 00:48:00.599 --> 00:48:04.680 | |
| essentially | |
| 00:48:02.720 --> 00:48:06.640 | |
| text the other one is search in | |
| 00:48:04.680 --> 00:48:08.559 | |
| continuous space so you find a prompt | |
| 00:48:06.640 --> 00:48:10.680 | |
| that isn't actually comprehensible text | |
| 00:48:08.559 --> 00:48:14.760 | |
| but nonetheless is a good | |
| 00:48:10.680 --> 00:48:16.960 | |
| prompt so looking at manual engineering | |
| 00:48:14.760 --> 00:48:19.000 | |
| um making sure that the format matches | |
| 00:48:16.960 --> 00:48:21.680 | |
| that of a trained model uh such as the | |
| 00:48:19.000 --> 00:48:24.359 | |
| chat format is actually really really | |
| 00:48:21.680 --> 00:48:26.119 | |
| important um and this can have a a large | |
| 00:48:24.359 --> 00:48:28.119 | |
| effect on models there's a really paper | |
| 00:48:26.119 --> 00:48:30.000 | |
| that demonstrated this convincingly | |
| 00:48:28.119 --> 00:48:33.200 | |
| before and also releases some software | |
| 00:48:30.000 --> 00:48:35.880 | |
| that allows you to do this um kind of in | |
| 00:48:33.200 --> 00:48:38.079 | |
| an efficient manner and what this is | |
| 00:48:35.880 --> 00:48:41.200 | |
| showing is | |
| 00:48:38.079 --> 00:48:45.079 | |
| um this is the original formatting of a | |
| 00:48:41.200 --> 00:48:48.400 | |
| prompt that was given I I Believe by uh | |
| 00:48:45.079 --> 00:48:50.119 | |
| some sort of like uh machine reading or | |
| 00:48:48.400 --> 00:48:52.799 | |
| document based question answering data | |
| 00:48:50.119 --> 00:48:55.480 | |
| set which was like passage | |
| 00:48:52.799 --> 00:48:58.440 | |
| answer if you modify the spacing between | |
| 00:48:55.480 --> 00:49:01.680 | |
| the the fields that increases your score | |
| 00:48:58.440 --> 00:49:04.280 | |
| by several percentage points um if you | |
| 00:49:01.680 --> 00:49:06.880 | |
| remove the colons that increases your | |
| 00:49:04.280 --> 00:49:08.720 | |
| score by a few more percentage points | |
| 00:49:06.880 --> 00:49:10.119 | |
| it's kind of silly but like little | |
| 00:49:08.720 --> 00:49:11.040 | |
| things like this actually can make a | |
| 00:49:10.119 --> 00:49:14.240 | |
| really big | |
| 00:49:11.040 --> 00:49:17.599 | |
| difference um if you modify the casing | |
| 00:49:14.240 --> 00:49:19.960 | |
| this decreases by a lot if you modify | |
| 00:49:17.599 --> 00:49:22.440 | |
| the casing and remove colons so the | |
| 00:49:19.960 --> 00:49:25.200 | |
| thing that was useful like adding colons | |
| 00:49:22.440 --> 00:49:26.720 | |
| here remove colons uh that further | |
| 00:49:25.200 --> 00:49:29.280 | |
| decrease | |
| 00:49:26.720 --> 00:49:31.400 | |
| if you forget to add a space between the | |
| 00:49:29.280 --> 00:49:32.559 | |
| passage and the text that really hurts | |
| 00:49:31.400 --> 00:49:35.599 | |
| your | |
| 00:49:32.559 --> 00:49:38.000 | |
| accuracy so this is pretty painful right | |
| 00:49:35.599 --> 00:49:40.599 | |
| like you don't want to be getting uh | |
| 00:49:38.000 --> 00:49:44.160 | |
| 0.036% accuracy when adding a space | |
| 00:49:40.599 --> 00:49:48.680 | |
| would give you like 75% accuracy | |
| 00:49:44.160 --> 00:49:50.799 | |
| right um and one interesting thing is um | |
| 00:49:48.680 --> 00:49:53.160 | |
| this is looking | |
| 00:49:50.799 --> 00:49:56.559 | |
| at different | |
| 00:49:53.160 --> 00:49:58.520 | |
| models and um | |
| 00:49:56.559 --> 00:50:00.640 | |
| with different models it's pretty | |
| 00:49:58.520 --> 00:50:03.599 | |
| consistent that many different plausible | |
| 00:50:00.640 --> 00:50:05.400 | |
| formats that you try the average gives | |
| 00:50:03.599 --> 00:50:07.240 | |
| you a really low accuracy but there's a | |
| 00:50:05.400 --> 00:50:08.760 | |
| few outliers that give you really good | |
| 00:50:07.240 --> 00:50:11.119 | |
| accuracy and these probably correspond | |
| 00:50:08.760 --> 00:50:13.400 | |
| to the things that it was trained on um | |
| 00:50:11.119 --> 00:50:15.880 | |
| instruction tuned on or or other things | |
| 00:50:13.400 --> 00:50:17.480 | |
| like this so number one make sure you're | |
| 00:50:15.880 --> 00:50:19.799 | |
| using like the canonical prompt | |
| 00:50:17.480 --> 00:50:21.240 | |
| formatting for the model for sure number | |
| 00:50:19.799 --> 00:50:22.640 | |
| two you might want to do a little bit of | |
| 00:50:21.240 --> 00:50:24.720 | |
| additional search to see if you can do | |
| 00:50:22.640 --> 00:50:26.960 | |
| even better than that so um this is | |
| 00:50:24.720 --> 00:50:29.480 | |
| something to be very aware | |
| 00:50:26.960 --> 00:50:32.480 | |
| of | |
| 00:50:29.480 --> 00:50:32.480 | |
| um | |
| 00:50:34.200 --> 00:50:37.680 | |
| okay do you have a | |
| 00:50:39.599 --> 00:50:43.720 | |
| question this is dependent on what it | |
| 00:50:41.720 --> 00:50:47.680 | |
| sees in trading time another thing | |
| 00:50:43.720 --> 00:50:51.920 | |
| actually is um this will definitely be | |
| 00:50:47.680 --> 00:50:53.200 | |
| tighter for uh like a chat GPT or GPT 4 | |
| 00:50:51.920 --> 00:50:56.599 | |
| um because it's been trained on many | |
| 00:50:53.200 --> 00:50:59.319 | |
| different formats at training time um | |
| 00:50:56.599 --> 00:51:00.880 | |
| and so the better the model has been | |
| 00:50:59.319 --> 00:51:03.520 | |
| trained on a lot of different formats | |
| 00:51:00.880 --> 00:51:05.559 | |
| the less this is going to have an | |
| 00:51:03.520 --> 00:51:06.920 | |
| effect but you know you're probably not | |
| 00:51:05.559 --> 00:51:09.440 | |
| going to be retraining a model that | |
| 00:51:06.920 --> 00:51:10.799 | |
| somebody gives you uh so like this is | |
| 00:51:09.440 --> 00:51:12.880 | |
| something to be very aware of if you're | |
| 00:51:10.799 --> 00:51:14.839 | |
| just a downstream newer model especially | |
| 00:51:12.880 --> 00:51:17.599 | |
| an open source | |
| 00:51:14.839 --> 00:51:19.359 | |
| model um another thing is how do you | |
| 00:51:17.599 --> 00:51:22.280 | |
| give instructions to | |
| 00:51:19.359 --> 00:51:25.000 | |
| models um instructions should be clear | |
| 00:51:22.280 --> 00:51:29.280 | |
| concise and easy to understand one very | |
| 00:51:25.000 --> 00:51:31.559 | |
| funny thing is um I think now like | |
| 00:51:29.280 --> 00:51:33.280 | |
| actually prompting language models is | |
| 00:51:31.559 --> 00:51:34.960 | |
| very similar to prompting humans | |
| 00:51:33.280 --> 00:51:37.119 | |
| especially if we're talking about like | |
| 00:51:34.960 --> 00:51:38.760 | |
| gp4 so if you're not very good at | |
| 00:51:37.119 --> 00:51:41.599 | |
| explaining things to humans that might | |
| 00:51:38.760 --> 00:51:45.440 | |
| actually be bad um and you might want to | |
| 00:51:41.599 --> 00:51:47.359 | |
| practice that and explaining things to | |
| 00:51:45.440 --> 00:51:50.319 | |
| models might be a good way to practice | |
| 00:51:47.359 --> 00:51:51.799 | |
| that right so you know um it actually | |
| 00:51:50.319 --> 00:51:54.040 | |
| can give you feedback without annoying | |
| 00:51:51.799 --> 00:51:55.359 | |
| your friends by having you explain uh | |
| 00:51:54.040 --> 00:51:58.160 | |
| things to them in several different ways | |
| 00:51:55.359 --> 00:52:00.040 | |
| way and seeing how they react so um but | |
| 00:51:58.160 --> 00:52:03.680 | |
| anyway clear concise easy to understand | |
| 00:52:00.040 --> 00:52:05.319 | |
| is good um there's this prompting guide | |
| 00:52:03.680 --> 00:52:08.599 | |
| uh which I I can | |
| 00:52:05.319 --> 00:52:13.240 | |
| open um this has a prompt engineering | |
| 00:52:08.599 --> 00:52:14.520 | |
| guide I I I like this site but it it | |
| 00:52:13.240 --> 00:52:17.400 | |
| does have a bit | |
| 00:52:14.520 --> 00:52:18.880 | |
| of like variance in the importance of | |
| 00:52:17.400 --> 00:52:21.760 | |
| the information that tells you but like | |
| 00:52:18.880 --> 00:52:23.960 | |
| this particular page is nice I feel so | |
| 00:52:21.760 --> 00:52:26.160 | |
| start simple start with simple | |
| 00:52:23.960 --> 00:52:29.520 | |
| instructions um | |
| 00:52:26.160 --> 00:52:32.119 | |
| you should tell the model what it should | |
| 00:52:29.520 --> 00:52:36.839 | |
| be doing so make sure you say write | |
| 00:52:32.119 --> 00:52:39.799 | |
| classify summarize translate order um | |
| 00:52:36.839 --> 00:52:41.960 | |
| and things like this uh it also gives | |
| 00:52:39.799 --> 00:52:45.440 | |
| some good examples of the level of | |
| 00:52:41.960 --> 00:52:47.559 | |
| specificity that you should be giving so | |
| 00:52:45.440 --> 00:52:49.680 | |
| something that's less precise is explain | |
| 00:52:47.559 --> 00:52:51.559 | |
| the concept of prompt engineering keep | |
| 00:52:49.680 --> 00:52:53.920 | |
| the explanation short only a few | |
| 00:52:51.559 --> 00:52:57.119 | |
| sentences and don't be too | |
| 00:52:53.920 --> 00:52:58.799 | |
| descriptive um it use two to three | |
| 00:52:57.119 --> 00:53:00.240 | |
| sentences to explain the concept of | |
| 00:52:58.799 --> 00:53:02.599 | |
| prompt engineering to a high school | |
| 00:53:00.240 --> 00:53:04.839 | |
| student so what this does is this tells | |
| 00:53:02.599 --> 00:53:07.839 | |
| you the level of read like the reading | |
| 00:53:04.839 --> 00:53:07.839 | |
| level | |
| 00:53:07.960 --> 00:53:12.520 | |
| um so this doesn't even tell you the | |
| 00:53:10.200 --> 00:53:14.319 | |
| reading level I guess um and then two to | |
| 00:53:12.520 --> 00:53:16.240 | |
| three sentences is more precise than | |
| 00:53:14.319 --> 00:53:19.200 | |
| keep it a few sentences don't be too | |
| 00:53:16.240 --> 00:53:22.440 | |
| descriptive so um the more precise you | |
| 00:53:19.200 --> 00:53:25.760 | |
| can be the the better it is um one | |
| 00:53:22.440 --> 00:53:27.040 | |
| interesting thing is like if you ask | |
| 00:53:25.760 --> 00:53:28.359 | |
| your friend to do something and they | |
| 00:53:27.040 --> 00:53:32.400 | |
| don't know how to do it they'll complain | |
| 00:53:28.359 --> 00:53:34.240 | |
| to you but right now uh LMS don't | |
| 00:53:32.400 --> 00:53:35.720 | |
| complain to you they may in the future | |
| 00:53:34.240 --> 00:53:38.680 | |
| uh that might be like actually an | |
| 00:53:35.720 --> 00:53:40.799 | |
| interesting thing to find uh the you | |
| 00:53:38.680 --> 00:53:42.319 | |
| know interesting methodological thing to | |
| 00:53:40.799 --> 00:53:45.240 | |
| look at for a project or something like | |
| 00:53:42.319 --> 00:53:47.960 | |
| that but um right now you need to be | |
| 00:53:45.240 --> 00:53:49.040 | |
| precise and like there's it doesn't give | |
| 00:53:47.960 --> 00:53:51.799 | |
| you feedback when you're not being | |
| 00:53:49.040 --> 00:53:51.799 | |
| precise so you need | |
| 00:53:52.000 --> 00:53:56.359 | |
| to um separately from this there are | |
| 00:53:54.200 --> 00:53:59.160 | |
| methods for automatic prompt engineering | |
| 00:53:56.359 --> 00:54:00.960 | |
| so uh prompt paraphrasing gradient based | |
| 00:53:59.160 --> 00:54:02.240 | |
| discreet prompt search prompt tuning | |
| 00:54:00.960 --> 00:54:06.160 | |
| prefix | |
| 00:54:02.240 --> 00:54:09.880 | |
| tuning so prompt paraphrasing um this is | |
| 00:54:06.160 --> 00:54:12.559 | |
| a method that uh we proposed a while ago | |
| 00:54:09.880 --> 00:54:15.760 | |
| um to basically paraphrase an existing | |
| 00:54:12.559 --> 00:54:17.280 | |
| prompt to get other candidates um it's | |
| 00:54:15.760 --> 00:54:19.240 | |
| rather simple basically you take a | |
| 00:54:17.280 --> 00:54:21.960 | |
| prompt you put it through a paraphrasing | |
| 00:54:19.240 --> 00:54:24.280 | |
| model and it will give you new prompts | |
| 00:54:21.960 --> 00:54:25.440 | |
| and this is good because it will tend to | |
| 00:54:24.280 --> 00:54:28.319 | |
| give you things that are natural | |
| 00:54:25.440 --> 00:54:29.839 | |
| language um you can paraphrase 50 times | |
| 00:54:28.319 --> 00:54:32.480 | |
| try all of them see which one gives you | |
| 00:54:29.839 --> 00:54:37.079 | |
| the highest accuracy and then use that | |
| 00:54:32.480 --> 00:54:39.280 | |
| one um there's also an interesting paper | |
| 00:54:37.079 --> 00:54:43.079 | |
| uh that demonstrates that you can do | |
| 00:54:39.280 --> 00:54:45.240 | |
| this iteratively so you paraphrase once | |
| 00:54:43.079 --> 00:54:46.599 | |
| um you filter down all the candidates | |
| 00:54:45.240 --> 00:54:48.119 | |
| that do well and then you go in and | |
| 00:54:46.599 --> 00:54:49.960 | |
| paraphrase them again and you just do | |
| 00:54:48.119 --> 00:54:51.960 | |
| this over and over again and that can | |
| 00:54:49.960 --> 00:54:54.079 | |
| give you better results than kind of one | |
| 00:54:51.960 --> 00:54:57.079 | |
| one off | |
| 00:54:54.079 --> 00:54:57.079 | |
| paraphrasing | |
| 00:54:59.240 --> 00:55:02.079 | |
| so that's very simple you can even use a | |
| 00:55:01.079 --> 00:55:04.160 | |
| large language model to do the | |
| 00:55:02.079 --> 00:55:06.599 | |
| paraphrasing for you um another thing | |
| 00:55:04.160 --> 00:55:08.920 | |
| that you can do is gradient based search | |
| 00:55:06.599 --> 00:55:11.119 | |
| so the way this works is you need to | |
| 00:55:08.920 --> 00:55:16.319 | |
| have a a model that you can calculate | |
| 00:55:11.119 --> 00:55:19.920 | |
| gradients for and what you do is you | |
| 00:55:16.319 --> 00:55:22.240 | |
| calculate you create a seed prompt and | |
| 00:55:19.920 --> 00:55:26.000 | |
| then you calculate gradients into that | |
| 00:55:22.240 --> 00:55:29.760 | |
| seed prompt so you treat the | |
| 00:55:26.000 --> 00:55:33.160 | |
| um you treat each of the tokens here | |
| 00:55:29.760 --> 00:55:36.680 | |
| like T1 T2 T3 T4 | |
| 00:55:33.160 --> 00:55:38.240 | |
| T5 as their own embeddings you do back | |
| 00:55:36.680 --> 00:55:39.920 | |
| propop into those embeddings and you | |
| 00:55:38.240 --> 00:55:42.799 | |
| optimize them to get high accuracy on | |
| 00:55:39.920 --> 00:55:44.720 | |
| your data set then after you're done | |
| 00:55:42.799 --> 00:55:47.319 | |
| optimizing them to get high accuracy on | |
| 00:55:44.720 --> 00:55:49.079 | |
| your data set you clamp them onto the | |
| 00:55:47.319 --> 00:55:52.160 | |
| nearest neighbor embedding that you | |
| 00:55:49.079 --> 00:55:53.520 | |
| already have so you basically say okay | |
| 00:55:52.160 --> 00:55:56.720 | |
| the nearest neighbor to the embedding | |
| 00:55:53.520 --> 00:55:58.920 | |
| that I learned you um is atmosphere then | |
| 00:55:56.720 --> 00:56:02.240 | |
| a lot dialogue clone | |
| 00:55:58.920 --> 00:56:03.799 | |
| totally and so this is this will | |
| 00:56:02.240 --> 00:56:05.599 | |
| actually give you better results than | |
| 00:56:03.799 --> 00:56:07.839 | |
| paraphrasing in many cases because the | |
| 00:56:05.599 --> 00:56:11.520 | |
| search space is less constrained you can | |
| 00:56:07.839 --> 00:56:12.960 | |
| get these very unnatural prompts uh that | |
| 00:56:11.520 --> 00:56:16.280 | |
| don't seem to make sense but actually | |
| 00:56:12.960 --> 00:56:20.280 | |
| work well this has particularly been | |
| 00:56:16.280 --> 00:56:22.960 | |
| widely used in um adversarial attacks on | |
| 00:56:20.280 --> 00:56:25.599 | |
| language modals so how can you come up | |
| 00:56:22.960 --> 00:56:27.720 | |
| with um | |
| 00:56:25.599 --> 00:56:31.559 | |
| with prompts that | |
| 00:56:27.720 --> 00:56:33.319 | |
| cause language models to uh do things | |
| 00:56:31.559 --> 00:56:36.039 | |
| that you don't want them to be | |
| 00:56:33.319 --> 00:56:38.920 | |
| doing and um there's actually this nice | |
| 00:56:36.039 --> 00:56:41.440 | |
| paper uh also by people at CMU called | |
| 00:56:38.920 --> 00:56:42.960 | |
| Universal and transferable adversarial | |
| 00:56:41.440 --> 00:56:45.400 | |
| attacks on line language | |
| 00:56:42.960 --> 00:56:50.559 | |
| models and basically what they do is | |
| 00:56:45.400 --> 00:56:53.880 | |
| they try to optimize the uh they try to | |
| 00:56:50.559 --> 00:56:56.839 | |
| optimize the prompt to create a prompt | |
| 00:56:53.880 --> 00:56:58.599 | |
| that causes the model to do bad things | |
| 00:56:56.839 --> 00:57:00.039 | |
| basically and they try to do it even on | |
| 00:56:58.599 --> 00:57:03.440 | |
| models that have been trying to not do | |
| 00:57:00.039 --> 00:57:05.039 | |
| bad things and they demonstrate that | |
| 00:57:03.440 --> 00:57:07.359 | |
| number one you can cause things like | |
| 00:57:05.039 --> 00:57:09.599 | |
| models like llama to do bad you know bad | |
| 00:57:07.359 --> 00:57:12.559 | |
| things like output toxic things tell you | |
| 00:57:09.599 --> 00:57:15.599 | |
| how to build bombs stuff like that but | |
| 00:57:12.559 --> 00:57:18.480 | |
| also the same prompts also work on like | |
| 00:57:15.599 --> 00:57:22.319 | |
| GPD models uh which is kind of like | |
| 00:57:18.480 --> 00:57:23.839 | |
| interesting and and very uh you know | |
| 00:57:22.319 --> 00:57:26.520 | |
| confusing in a way because you thought | |
| 00:57:23.839 --> 00:57:28.160 | |
| this might be explo idiosyncrasies of a | |
| 00:57:26.520 --> 00:57:32.440 | |
| particular language model but actually | |
| 00:57:28.160 --> 00:57:32.440 | |
| it's not so I I find this kind of | |
| 00:57:33.880 --> 00:57:39.520 | |
| fascinating | |
| 00:57:36.039 --> 00:57:42.240 | |
| so if you take that a step further one | |
| 00:57:39.520 --> 00:57:44.079 | |
| thing that you can do is you can say oh | |
| 00:57:42.240 --> 00:57:46.280 | |
| actually there's no reason why we need | |
| 00:57:44.079 --> 00:57:48.520 | |
| to clamp these embeddings back to an | |
| 00:57:46.280 --> 00:57:52.240 | |
| existing embedding right so we could | |
| 00:57:48.520 --> 00:57:56.079 | |
| just optimize the prompts the embeddings | |
| 00:57:52.240 --> 00:57:57.720 | |
| of the prompts that go for a task and | |
| 00:57:56.079 --> 00:58:02.000 | |
| not clamp them back to embeddings and | |
| 00:57:57.720 --> 00:58:03.599 | |
| just keep them as is so um what I mean | |
| 00:58:02.000 --> 00:58:07.079 | |
| by that is like right here it's | |
| 00:58:03.599 --> 00:58:09.160 | |
| optimizing T1 T2 T3 T4 T5 and then | |
| 00:58:07.079 --> 00:58:11.359 | |
| clamping that back to Atmosphere a lot | |
| 00:58:09.160 --> 00:58:13.960 | |
| dialog clone totally but just keep them | |
| 00:58:11.359 --> 00:58:16.160 | |
| as is and don't worry about them like | |
| 00:58:13.960 --> 00:58:18.039 | |
| actually being a token in the model | |
| 00:58:16.160 --> 00:58:19.400 | |
| because if you have control over your | |
| 00:58:18.039 --> 00:58:21.200 | |
| model you can just add them as new | |
| 00:58:19.400 --> 00:58:25.960 | |
| elements in the vocabulary and you're | |
| 00:58:21.200 --> 00:58:28.440 | |
| fine right so what they demonstrate in | |
| 00:58:25.960 --> 00:58:31.520 | |
| this paper is that instead of taking | |
| 00:58:28.440 --> 00:58:33.440 | |
| your 11 billion parameter model and | |
| 00:58:31.520 --> 00:58:35.920 | |
| training the whole 11 billion parameter | |
| 00:58:33.440 --> 00:58:38.359 | |
| model for many different tasks on many | |
| 00:58:35.920 --> 00:58:40.079 | |
| different data sets they just train | |
| 00:58:38.359 --> 00:58:42.039 | |
| these prompts which are like 20K | |
| 00:58:40.079 --> 00:58:44.039 | |
| parameters each I I forget how long it | |
| 00:58:42.039 --> 00:58:46.280 | |
| is it's like 10 tokens or 20 tokens or | |
| 00:58:44.039 --> 00:58:48.079 | |
| something like that um and train it on | |
| 00:58:46.280 --> 00:58:49.640 | |
| all of the the data sets here and you | |
| 00:58:48.079 --> 00:58:50.680 | |
| don't actually need to do multitask | |
| 00:58:49.640 --> 00:58:52.200 | |
| learning you don't need to train on | |
| 00:58:50.680 --> 00:58:53.720 | |
| multiple tasks at the same time you can | |
| 00:58:52.200 --> 00:58:56.119 | |
| just train on a single | |
| 00:58:53.720 --> 00:58:58.599 | |
| task | |
| 00:58:56.119 --> 00:59:01.000 | |
| so now let's take that even a step | |
| 00:58:58.599 --> 00:59:03.640 | |
| further so this is only training the | |
| 00:59:01.000 --> 00:59:06.359 | |
| embeddings that you input into the model | |
| 00:59:03.640 --> 00:59:08.160 | |
| there's a method called prefix tuning | |
| 00:59:06.359 --> 00:59:10.319 | |
| and the way prefix tuning works is | |
| 00:59:08.160 --> 00:59:12.280 | |
| instead of training only the embeddings | |
| 00:59:10.319 --> 00:59:14.799 | |
| that go into the model they actually | |
| 00:59:12.280 --> 00:59:18.920 | |
| train a prefix that you then append to | |
| 00:59:14.799 --> 00:59:20.839 | |
| every layer of the model so prompt | |
| 00:59:18.920 --> 00:59:23.319 | |
| tuning basically does this for the first | |
| 00:59:20.839 --> 00:59:24.839 | |
| layer of the model prefix tuning does | |
| 00:59:23.319 --> 00:59:28.400 | |
| this for every layer of the model you | |
| 00:59:24.839 --> 00:59:30.319 | |
| append a prefix uh for every day so it's | |
| 00:59:28.400 --> 00:59:32.200 | |
| just a more expressive version of | |
| 00:59:30.319 --> 00:59:36.119 | |
| prompting | |
| 00:59:32.200 --> 00:59:40.200 | |
| essentially so these are all kinds of | |
| 00:59:36.119 --> 00:59:43.680 | |
| gradual steps from a human created | |
| 00:59:40.200 --> 00:59:47.880 | |
| prompt into something that is basically | |
| 00:59:43.680 --> 00:59:50.839 | |
| training a a prompt or a prefix to the | |
| 00:59:47.880 --> 00:59:52.960 | |
| model so I I would take questions but | |
| 00:59:50.839 --> 00:59:55.200 | |
| let me get to the end of this section uh | |
| 00:59:52.960 --> 00:59:58.839 | |
| also because uh I think there's | |
| 00:59:55.200 --> 01:00:00.720 | |
| interesting analogies here so in the | |
| 00:59:58.839 --> 01:00:02.880 | |
| next class I'm going to talk about | |
| 01:00:00.720 --> 01:00:04.440 | |
| parameter efficient fine-tuning methods | |
| 01:00:02.880 --> 01:00:06.960 | |
| which is kind of a more | |
| 01:00:04.440 --> 01:00:10.000 | |
| General it's a more | |
| 01:00:06.960 --> 01:00:11.480 | |
| General version of prompt tuning or | |
| 01:00:10.000 --> 01:00:13.280 | |
| prefix tuning there are methods that | |
| 01:00:11.480 --> 01:00:15.960 | |
| tune a small number of parameters to get | |
| 01:00:13.280 --> 01:00:17.400 | |
| the model to do something and there's a | |
| 01:00:15.960 --> 01:00:18.880 | |
| bunch of different parameter efficient | |
| 01:00:17.400 --> 01:00:21.520 | |
| tuning methods many people may have | |
| 01:00:18.880 --> 01:00:23.880 | |
| heard of something like Laura uh or | |
| 01:00:21.520 --> 01:00:25.440 | |
| adapters um I just talked about prefix | |
| 01:00:23.880 --> 01:00:28.119 | |
| tuning | |
| 01:00:25.440 --> 01:00:30.960 | |
| so essentially prompt tuning and prefix | |
| 01:00:28.119 --> 01:00:33.359 | |
| tuning are part of this more General | |
| 01:00:30.960 --> 01:00:36.680 | |
| class of parameter efficient find tuning | |
| 01:00:33.359 --> 01:00:39.240 | |
| methods and so what we can say is | |
| 01:00:36.680 --> 01:00:41.119 | |
| actually prompting is fine-tuning | |
| 01:00:39.240 --> 01:00:42.920 | |
| prompting is a way of fine-tuning the | |
| 01:00:41.119 --> 01:00:46.799 | |
| model or getting the model to perform a | |
| 01:00:42.920 --> 01:00:49.839 | |
| particular task um and we have this | |
| 01:00:46.799 --> 01:00:53.720 | |
| taxonomy of we have prompts in natural | |
| 01:00:49.839 --> 01:00:55.160 | |
| language that are created uh by humans | |
| 01:00:53.720 --> 01:00:57.240 | |
| actually maybe I should say manual | |
| 01:00:55.160 --> 01:00:59.559 | |
| prompt engineering here this was first | |
| 01:00:57.240 --> 01:01:01.480 | |
| done in the gpd2 paper where they | |
| 01:00:59.559 --> 01:01:04.359 | |
| demonstrate that models uh models could | |
| 01:01:01.480 --> 01:01:06.200 | |
| solve tasks by doing it this way prompt | |
| 01:01:04.359 --> 01:01:07.760 | |
| paraphrasing is a step up from this | |
| 01:01:06.200 --> 01:01:09.799 | |
| because it's no longer relying on human | |
| 01:01:07.760 --> 01:01:12.680 | |
| engineering and you can you know expand | |
| 01:01:09.799 --> 01:01:15.280 | |
| to a broader set of prompts um it can | |
| 01:01:12.680 --> 01:01:17.359 | |
| always start with human created prompts | |
| 01:01:15.280 --> 01:01:20.240 | |
| so it's kind of like broader uh than | |
| 01:01:17.359 --> 01:01:21.799 | |
| that discrete prompt search doesn't | |
| 01:01:20.240 --> 01:01:23.599 | |
| necessarily need to rely on a | |
| 01:01:21.799 --> 01:01:25.559 | |
| paraphrasing model it could rely on like | |
| 01:01:23.599 --> 01:01:26.760 | |
| gradient-based models or something else | |
| 01:01:25.559 --> 01:01:29.240 | |
| like that to give you something that's | |
| 01:01:26.760 --> 01:01:32.559 | |
| not actually natural language uh kind of | |
| 01:01:29.240 --> 01:01:35.920 | |
| just random tokens continuous prompts or | |
| 01:01:32.559 --> 01:01:38.119 | |
| prompt tuning is a step above that | |
| 01:01:35.920 --> 01:01:41.039 | |
| multi-layer continuous prompts or prefix | |
| 01:01:38.119 --> 01:01:42.520 | |
| tuning is a layer above that parameter | |
| 01:01:41.039 --> 01:01:43.520 | |
| efficient tuning is more General than | |
| 01:01:42.520 --> 01:01:45.359 | |
| that and then you have all training | |
| 01:01:43.520 --> 01:01:49.160 | |
| methods so including fine tuning your | |
| 01:01:45.359 --> 01:01:52.680 | |
| model and so what are the implications | |
| 01:01:49.160 --> 01:01:55.760 | |
| of this um I think so a lot of people | |
| 01:01:52.680 --> 01:01:58.720 | |
| when prompting came out they were like | |
| 01:01:55.760 --> 01:02:00.640 | |
| prompting methods are very hacky I don't | |
| 01:01:58.720 --> 01:02:03.839 | |
| like how we have to do manual prompt | |
| 01:02:00.640 --> 01:02:08.160 | |
| engineering um it seems like a dark art | |
| 01:02:03.839 --> 01:02:11.000 | |
| as opposed to like you know actually you | |
| 01:02:08.160 --> 01:02:14.160 | |
| know some sort of well understood | |
| 01:02:11.000 --> 01:02:16.839 | |
| fine-tuning method that we could use um | |
| 01:02:14.160 --> 01:02:20.520 | |
| but I I actually like them I like | |
| 01:02:16.839 --> 01:02:23.920 | |
| prompting a lot because um if anybody is | |
| 01:02:20.520 --> 01:02:25.960 | |
| familiar with like basian basian | |
| 01:02:23.920 --> 01:02:27.920 | |
| statistics or machine learning we have | |
| 01:02:25.960 --> 01:02:28.799 | |
| the concept of like a prior probability | |
| 01:02:27.920 --> 01:02:31.200 | |
| over | |
| 01:02:28.799 --> 01:02:32.359 | |
| parameters and then a probability that | |
| 01:02:31.200 --> 01:02:34.680 | |
| we get | |
| 01:02:32.359 --> 01:02:37.880 | |
| after after fine tuning the model or | |
| 01:02:34.680 --> 01:02:40.440 | |
| after training the model and prompts in | |
| 01:02:37.880 --> 01:02:42.640 | |
| a way are our first like good prior over | |
| 01:02:40.440 --> 01:02:43.880 | |
| neural network models they give us the | |
| 01:02:42.640 --> 01:02:46.319 | |
| ability to | |
| 01:02:43.880 --> 01:02:48.559 | |
| specify what task the model should be | |
| 01:02:46.319 --> 01:02:51.880 | |
| doing or like a general idea of what | |
| 01:02:48.559 --> 01:02:54.200 | |
| task the model should be doing before we | |
| 01:02:51.880 --> 01:02:56.359 | |
| ask the model to actually do the task | |
| 01:02:54.200 --> 01:02:58.640 | |
| and and so we can either use that prior | |
| 01:02:56.359 --> 01:03:02.119 | |
| Asis we can use a prompted model Asis | |
| 01:02:58.640 --> 01:03:04.839 | |
| without doing any additional tuning or | |
| 01:03:02.119 --> 01:03:06.480 | |
| we could take the prior that we have | |
| 01:03:04.839 --> 01:03:07.920 | |
| given to the model by using a natural | |
| 01:03:06.480 --> 01:03:09.039 | |
| language description of the task it | |
| 01:03:07.920 --> 01:03:12.079 | |
| should be | |
| 01:03:09.039 --> 01:03:14.799 | |
| doing and then combine it with fineing | |
| 01:03:12.079 --> 01:03:17.039 | |
| so we can take the prompted | |
| 01:03:14.799 --> 01:03:19.279 | |
| model we can | |
| 01:03:17.039 --> 01:03:21.640 | |
| initialize we can initialize the | |
| 01:03:19.279 --> 01:03:23.960 | |
| distribution of this like Cas a prompt | |
| 01:03:21.640 --> 01:03:25.720 | |
| using the prompt using a human created | |
| 01:03:23.960 --> 01:03:28.160 | |
| prompt and then go on and fine-tune it | |
| 01:03:25.720 --> 01:03:30.960 | |
| on lots of training data as well and | |
| 01:03:28.160 --> 01:03:33.799 | |
| there's a method for doing that um by | |
| 01:03:30.960 --> 01:03:35.880 | |
| shik and schutza uh called uh pattern | |
| 01:03:33.799 --> 01:03:37.559 | |
| exploiting training where they do | |
| 01:03:35.880 --> 01:03:39.799 | |
| exactly that they basically initialize | |
| 01:03:37.559 --> 01:03:41.720 | |
| with a manually created prompt and then | |
| 01:03:39.799 --> 01:03:44.559 | |
| they find the model on finding inator | |
| 01:03:41.720 --> 01:03:46.400 | |
| after that so um that's a reason why I | |
| 01:03:44.559 --> 01:03:47.920 | |
| like prompting based methods they they | |
| 01:03:46.400 --> 01:03:49.720 | |
| give us this like really nice way to | |
| 01:03:47.920 --> 01:03:53.039 | |
| very quickly create a system but we can | |
| 01:03:49.720 --> 01:03:56.079 | |
| also have you know whatever level of | |
| 01:03:53.039 --> 01:03:59.880 | |
| additional training on top of that | |
| 01:03:56.079 --> 01:03:59.880 | |
| cool so that's a little bit early I'm | |