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WEBVTT
00:00:00.480 --> 00:00:06.279
so uh I guess we can get started uh
00:00:04.080 --> 00:00:09.880
today I'm going to be talking about code
00:00:06.279 --> 00:00:11.719
generation and uh so this is a a
00:00:09.880 --> 00:00:13.599
research topic that I've uh worked on
00:00:11.719 --> 00:00:15.280
for a long time now I I like a lot it's
00:00:13.599 --> 00:00:17.520
become very useful nowadays which is
00:00:15.280 --> 00:00:20.960
very exciting um so I'd like to talk
00:00:17.520 --> 00:00:23.119
about kind of some of the basics and
00:00:20.960 --> 00:00:28.000
Frontiers uh that we're working on right
00:00:23.119 --> 00:00:28.000
now in this General uh area
00:00:31.719 --> 00:00:36.760
um
00:00:33.360 --> 00:00:38.160
so before I get into code generation
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specifically one thing I'd like to point
00:00:38.160 --> 00:00:43.399
out is for the next four or so classes
00:00:40.719 --> 00:00:45.680
I'm going to be talking about tasks and
00:00:43.399 --> 00:00:48.680
up until now I've been focusing on a lot
00:00:45.680 --> 00:00:52.840
of like General things that weren't as
00:00:48.680 --> 00:00:55.199
much about any specific tasks um
00:00:52.840 --> 00:00:57.000
and I know that not everybody's going to
00:00:55.199 --> 00:00:59.399
be interested in the four tasks that I'm
00:00:57.000 --> 00:01:00.960
talking about in the next you know four
00:00:59.399 --> 00:01:02.480
lectures
00:01:00.960 --> 00:01:04.920
um
00:01:02.480 --> 00:01:06.640
but I'm going to be covering various
00:01:04.920 --> 00:01:08.680
things about different tasks and
00:01:06.640 --> 00:01:10.640
hopefully you can map the same questions
00:01:08.680 --> 00:01:12.040
onto whatever task you are interested in
00:01:10.640 --> 00:01:14.360
if you're not interested in any of the
00:01:12.040 --> 00:01:15.880
ones I talk about here so basically what
00:01:14.360 --> 00:01:18.119
I want to talk about is the task
00:01:15.880 --> 00:01:21.040
objective like why do we do that task
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why is it important um what data sets
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can we use to train or test our models
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on these tasks evaluation metrics and
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how do we evaluate uh both manually and
00:01:28.799 --> 00:01:32.079
automatically with respect to how good
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we're
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doing and finally models and methods so
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you know how do we solve the
00:01:37.880 --> 00:01:42.479
problem and so for code generation first
00:01:40.720 --> 00:01:44.439
I'd like to talk about the overview and
00:01:42.479 --> 00:01:47.040
objectives of code generation so
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basically code generation is the task of
00:01:47.040 --> 00:01:52.439
generating executable code is an
00:01:48.840 --> 00:01:54.479
interface to uh a program or to
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computers and there's a lot of different
00:01:54.479 --> 00:02:01.000
ways we can do this um why do we want to
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do this so
00:02:01.000 --> 00:02:05.000
the first thing is that software
00:02:03.159 --> 00:02:06.759
engineering is really important and
00:02:05.000 --> 00:02:09.640
being able to generate code accelerate
00:02:06.759 --> 00:02:11.560
software engineering uh now code
00:02:09.640 --> 00:02:13.640
generation is practical and I hope that
00:02:11.560 --> 00:02:15.599
everybody in the class is using some
00:02:13.640 --> 00:02:17.840
sort of you know code generation to
00:02:15.599 --> 00:02:20.200
accelerate your own workflow if you're
00:02:17.840 --> 00:02:22.599
not I highly encourage you to to try it
00:02:20.200 --> 00:02:26.200
because it's very
00:02:22.599 --> 00:02:31.040
useful second it also does things like
00:02:26.200 --> 00:02:34.239
enabling models to access tools um
00:02:31.040 --> 00:02:37.440
and even if you're not specifically
00:02:34.239 --> 00:02:39.440
working on a software related task this
00:02:37.440 --> 00:02:41.000
can be helpful but I want to talk about
00:02:39.440 --> 00:02:42.480
this in a later class when we talk about
00:02:41.000 --> 00:02:46.640
llm agents so I'm not going to be
00:02:42.480 --> 00:02:48.319
talking about um that as much this time
00:02:46.640 --> 00:02:50.159
uh one other thing that I I forgot to
00:02:48.319 --> 00:02:52.920
mention here which I'm also going to
00:02:50.159 --> 00:02:55.000
talk about in the later class is even if
00:02:52.920 --> 00:02:58.120
you're not using code at all training on
00:02:55.000 --> 00:03:00.319
code has been shown to cause some
00:02:58.120 --> 00:03:01.920
benefits to learning models uh
00:03:00.319 --> 00:03:03.799
specifically with respect to learning
00:03:01.920 --> 00:03:06.480
like difficult multitask reasoning uh
00:03:03.799 --> 00:03:07.599
sorry multi-step reasoning tasks and so
00:03:06.480 --> 00:03:09.480
that's another reason why you might want
00:03:07.599 --> 00:03:10.840
to worry about codes so I'm going to
00:03:09.480 --> 00:03:12.840
mainly talk about the first one this
00:03:10.840 --> 00:03:14.560
time and leave the other two uh for
00:03:12.840 --> 00:03:17.720
future
00:03:14.560 --> 00:03:21.760
lectures so specifically for this task
00:03:17.720 --> 00:03:25.200
our input um is some sort of
00:03:21.760 --> 00:03:27.360
specification of what we want to do um
00:03:25.200 --> 00:03:30.319
and our output is going to be
00:03:27.360 --> 00:03:33.000
code so
00:03:30.319 --> 00:03:35.920
when you write a
00:03:33.000 --> 00:03:37.239
program how do you describe the thing
00:03:35.920 --> 00:03:40.239
that you want to implement in the
00:03:37.239 --> 00:03:42.000
program before you implement it like uh
00:03:40.239 --> 00:03:44.720
yeah what are some of the specifications
00:03:42.000 --> 00:03:44.720
that people can give
00:03:45.280 --> 00:03:50.720
you what the input and output of the
00:03:47.680 --> 00:03:52.360
functions are uh yes uh sorry what what
00:03:50.720 --> 00:03:54.400
types the inputs and outputs of the
00:03:52.360 --> 00:03:56.239
function are so those would be like type
00:03:54.400 --> 00:03:57.760
in in Python for example yeah that
00:03:56.239 --> 00:03:59.439
that's a good one it's actually not on
00:03:57.760 --> 00:04:02.079
my list of things here but it's it's a
00:03:59.439 --> 00:04:06.040
good Point yeah any any other things
00:04:02.079 --> 00:04:08.680
yeah complexity requirements complexity
00:04:06.040 --> 00:04:11.040
requirements constraints that is also
00:04:08.680 --> 00:04:14.840
not on my list of things here uh that's
00:04:11.040 --> 00:04:17.040
uh that's a good one too um and any uh
00:04:14.840 --> 00:04:20.280
slightly more straight forward
00:04:17.040 --> 00:04:24.040
things pseudo code yeah um in pseudo
00:04:20.280 --> 00:04:26.720
code uh what what is pseudo code written
00:04:24.040 --> 00:04:28.440
in natural natural language yeah so
00:04:26.720 --> 00:04:31.199
natural language inputs are are one
00:04:28.440 --> 00:04:34.520
thing so I will tell you I want I want a
00:04:31.199 --> 00:04:39.160
program that uh I want you to write a
00:04:34.520 --> 00:04:41.479
web interface that allows me to um order
00:04:39.160 --> 00:04:43.560
pizza or something like that that that
00:04:41.479 --> 00:04:46.560
would be one way to do it any other
00:04:43.560 --> 00:04:46.560
ideas
00:04:51.199 --> 00:04:55.840
yeah this is what I have and this is
00:04:53.360 --> 00:04:57.240
what I want yeah so um that's especially
00:04:55.840 --> 00:04:59.880
the case if you're like modifying a
00:04:57.240 --> 00:05:01.400
program um or something like that so
00:04:59.880 --> 00:05:06.280
actually the next one on my list there
00:05:01.400 --> 00:05:06.280
so good good point um any other
00:05:09.759 --> 00:05:15.720
ideas yeah or or a multimodal person you
00:05:12.880 --> 00:05:20.120
know I might say I want a pizza ordering
00:05:15.720 --> 00:05:22.039
I want a pizza ordering app and up here
00:05:20.120 --> 00:05:24.000
it should have your like username so you
00:05:22.039 --> 00:05:25.840
can click through the settings and like
00:05:24.000 --> 00:05:27.080
over here you should have the menu and
00:05:25.840 --> 00:05:28.680
over here you should have your check out
00:05:27.080 --> 00:05:30.400
card or something like that you know
00:05:28.680 --> 00:05:32.440
it's something you do for a programmer
00:05:30.400 --> 00:05:34.680
as well until recently we couldn't
00:05:32.440 --> 00:05:37.680
really use that with like actual models
00:05:34.680 --> 00:05:40.560
but um yeah yeah well that was my fourth
00:05:37.680 --> 00:05:42.639
one but um and then the other one uh
00:05:40.560 --> 00:05:44.960
inputs and outputs this could come in
00:05:42.639 --> 00:05:46.560
the form of like unit tests or something
00:05:44.960 --> 00:05:49.199
like that where it's like yeah this is
00:05:46.560 --> 00:05:51.160
the input this is the expected output so
00:05:49.199 --> 00:05:53.240
these are all things we use both as
00:05:51.160 --> 00:05:55.639
human programmers and in code generation
00:05:53.240 --> 00:05:58.120
models I really like the two other
00:05:55.639 --> 00:06:00.440
points though um
00:05:58.120 --> 00:06:03.759
because typin
00:06:00.440 --> 00:06:05.479
are actually something that you like
00:06:03.759 --> 00:06:06.599
writing writing with typ pints is
00:06:05.479 --> 00:06:09.240
actually something that you can do with
00:06:06.599 --> 00:06:14.120
code generation models and um
00:06:09.240 --> 00:06:16.680
constraints such as like it should it
00:06:14.120 --> 00:06:20.199
should meet certain speed requirements
00:06:16.680 --> 00:06:21.520
or it should um you know use certain
00:06:20.199 --> 00:06:22.960
libraries or something like that are
00:06:21.520 --> 00:06:24.840
also constraints that you could add I
00:06:22.960 --> 00:06:26.120
didn't put that on this slide here that
00:06:24.840 --> 00:06:28.319
might come in the natural language
00:06:26.120 --> 00:06:30.639
description but it could be something
00:06:28.319 --> 00:06:32.759
separate and then you know the output is
00:06:30.639 --> 00:06:36.759
whatever code you want
00:06:32.759 --> 00:06:38.240
to so um how many people are using like
00:06:36.759 --> 00:06:41.000
GitHub
00:06:38.240 --> 00:06:46.160
co-pilot like what
00:06:41.000 --> 00:06:47.759
percentage maybe about half okay um how
00:06:46.160 --> 00:06:49.840
many people are using another like
00:06:47.759 --> 00:06:56.080
assisted coding tool other than GitHub
00:06:49.840 --> 00:06:57.400
coet yeah g gp4 gp4 is an could be an
00:06:56.080 --> 00:06:58.680
assisted coding tool I'm talking more
00:06:57.400 --> 00:07:02.400
like something that's actually in your
00:06:58.680 --> 00:07:04.759
IDE something yeah anybody
00:07:02.400 --> 00:07:07.680
else does anyone use
00:07:04.759 --> 00:07:13.639
cursor no
00:07:07.680 --> 00:07:18.039
um yeah cursor yeah okay so
00:07:13.639 --> 00:07:20.919
yeah Co collab uh Ai and collab yeah so
00:07:18.039 --> 00:07:24.080
um so I think there are a lot of these
00:07:20.919 --> 00:07:26.879
uh going around I I use co-pilot myself
00:07:24.080 --> 00:07:28.639
I have not used cursor I do use GPD 4 um
00:07:26.879 --> 00:07:30.599
and I'll I'll show you an example of how
00:07:28.639 --> 00:07:32.919
I use them different
00:07:30.599 --> 00:07:34.360
um if you haven't used copilot hopefully
00:07:32.919 --> 00:07:39.599
this will
00:07:34.360 --> 00:07:42.599
work um I just made a a simple
00:07:39.599 --> 00:07:42.599
video
00:07:43.280 --> 00:07:49.520
oops okay that's not working but anyway
00:07:46.159 --> 00:07:51.000
you um you type your uh you know you
00:07:49.520 --> 00:07:54.319
type and it basically completes your
00:07:51.000 --> 00:07:56.639
code so this is this is an example here
00:07:54.319 --> 00:07:58.599
and I didn't write any of this code
00:07:56.639 --> 00:08:02.360
actually I just wrote the comments and
00:07:58.599 --> 00:08:04.000
then it filled in the the actual C and
00:08:02.360 --> 00:08:05.639
also I didn't exactly check if it's
00:08:04.000 --> 00:08:08.080
correct or not
00:08:05.639 --> 00:08:11.120
so if there's any mistake it's co
00:08:08.080 --> 00:08:15.159
Pilot's fault not my fault but um I it
00:08:11.120 --> 00:08:15.159
looked correct to me so
00:08:15.759 --> 00:08:21.120
um and oh by the way you get to use it
00:08:18.120 --> 00:08:22.800
for free with your CMU account so if you
00:08:21.120 --> 00:08:24.120
uh if you don't want to use it but don't
00:08:22.800 --> 00:08:25.919
want to pay for it you're and left
00:08:24.120 --> 00:08:31.639
because you can use
00:08:25.919 --> 00:08:36.320
it um another example uh is gd4 or uh
00:08:31.639 --> 00:08:38.519
more recently Cloud 3 um and basically
00:08:36.320 --> 00:08:40.680
this can do a different variety of
00:08:38.519 --> 00:08:43.719
things so we talked about screenshots
00:08:40.680 --> 00:08:45.720
and basically I asked Claude to create a
00:08:43.719 --> 00:08:48.399
react app that replicates the claw
00:08:45.720 --> 00:08:50.240
interface by giving it a screenshot and
00:08:48.399 --> 00:08:52.560
asking it create a react app that looks
00:08:50.240 --> 00:08:55.200
like the screenshot and then it gave me
00:08:52.560 --> 00:09:00.800
a whole bunch of text and in the end it
00:08:55.200 --> 00:09:03.320
started um making this uh container here
00:09:00.800 --> 00:09:08.040
um
00:09:03.320 --> 00:09:11.040
and this uh it basically is skipping
00:09:08.040 --> 00:09:12.800
some of the styling stuff uh because
00:09:11.040 --> 00:09:14.480
large language models I I think they're
00:09:12.800 --> 00:09:16.560
basically trained so that they don't
00:09:14.480 --> 00:09:19.959
give really really long responses
00:09:16.560 --> 00:09:21.320
because like if you uh asked for
00:09:19.959 --> 00:09:23.640
something that would take a really
00:09:21.320 --> 00:09:25.519
really long time and then the model just
00:09:23.640 --> 00:09:26.880
complied and gave that to you for a
00:09:25.519 --> 00:09:29.000
really really long time it would cost
00:09:26.880 --> 00:09:30.680
them a lot of money so I feel like they
00:09:29.000 --> 00:09:32.440
they B try to train the models to only
00:09:30.680 --> 00:09:37.160
out at like a thousand tokens at a time
00:09:32.440 --> 00:09:38.959
or something like that so um it it won't
00:09:37.160 --> 00:09:40.839
actually go out and program the whole
00:09:38.959 --> 00:09:43.120
project for you but with a little
00:09:40.839 --> 00:09:44.680
cajoling if you say okay now implement
00:09:43.120 --> 00:09:48.519
this part now implement this part now
00:09:44.680 --> 00:09:49.959
implement this part um you uh you can
00:09:48.519 --> 00:09:53.040
end up with some pretty interesting
00:09:49.959 --> 00:09:55.680
stuff and let me
00:09:53.040 --> 00:09:57.120
uh let me see if I can I can show you an
00:09:55.680 --> 00:10:01.320
example
00:09:57.120 --> 00:10:01.320
so I I know a little bit of
00:10:01.440 --> 00:10:07.040
react um the front end framework but I
00:10:04.240 --> 00:10:09.839
don't know a whole lot but recently
00:10:07.040 --> 00:10:14.279
we've been um working on an open-source
00:10:09.839 --> 00:10:18.959
assisted coding app and I most of this
00:10:14.279 --> 00:10:21.519
was just written by quad um it's uh I I
00:10:18.959 --> 00:10:23.079
said I want an app that on the left side
00:10:21.519 --> 00:10:26.160
it has a chat window and then on the
00:10:23.079 --> 00:10:28.240
right side it has three uh three panes
00:10:26.160 --> 00:10:30.120
one is a terminal one is a planner and
00:10:28.240 --> 00:10:32.200
one is a code editor
00:10:30.120 --> 00:10:33.880
and um so it gave me something it was
00:10:32.200 --> 00:10:37.399
kind of ugly so I said okay make the
00:10:33.880 --> 00:10:40.639
background black um change the CSS file
00:10:37.399 --> 00:10:43.639
so that um you have like a user icon and
00:10:40.639 --> 00:10:46.040
a robot icon and stuff like that and
00:10:43.639 --> 00:10:49.240
after this I I wrote very little of this
00:10:46.040 --> 00:10:51.079
code I wrote like 1% of this code or
00:10:49.240 --> 00:10:54.480
something like that and it's able to to
00:10:51.079 --> 00:10:57.880
do these sorts of things for you um so
00:10:54.480 --> 00:11:01.000
if you don't like writing front ends
00:10:57.880 --> 00:11:03.880
good luck uh or good good news that you
00:11:01.000 --> 00:11:05.560
uh can come up with a passable front end
00:11:03.880 --> 00:11:07.519
without uh without actually having to
00:11:05.560 --> 00:11:08.720
write it nonetheless you know good front
00:11:07.519 --> 00:11:10.200
end Engineers will come up with
00:11:08.720 --> 00:11:13.639
something much more beautiful than that
00:11:10.200 --> 00:11:15.880
so um so basically why do I why did I
00:11:13.639 --> 00:11:19.959
want to say this I think um GitHub
00:11:15.880 --> 00:11:20.839
co-pilot and Pla or gp4 serve very
00:11:19.959 --> 00:11:25.200
different
00:11:20.839 --> 00:11:27.360
purposes um GitHub co-pilot is code
00:11:25.200 --> 00:11:30.160
completion and it mostly works for
00:11:27.360 --> 00:11:32.440
shorter things so it's like your next
00:11:30.160 --> 00:11:34.760
thought in your code in code that you
00:11:32.440 --> 00:11:37.560
know pretty well something like plot or
00:11:34.760 --> 00:11:40.639
gp4 is much better for really long
00:11:37.560 --> 00:11:44.680
things um where you want to build like a
00:11:40.639 --> 00:11:47.040
full class or something like that and I
00:11:44.680 --> 00:11:48.480
also have found that if you're coding in
00:11:47.040 --> 00:11:50.079
a language that you're very familiar
00:11:48.480 --> 00:11:51.560
with copilot might be more useful
00:11:50.079 --> 00:11:52.959
because you want fine grain control and
00:11:51.560 --> 00:11:55.040
you want it to fill out things to make
00:11:52.959 --> 00:11:56.519
it faster whereas if you're coding in a
00:11:55.040 --> 00:11:58.040
language that you're not very familiar
00:11:56.519 --> 00:11:59.680
with something like Claud is good
00:11:58.040 --> 00:12:01.839
because you can write a whole you know
00:11:59.680 --> 00:12:04.800
program forties so these are the
00:12:01.839 --> 00:12:07.680
differences another thing is GitHub
00:12:04.800 --> 00:12:09.240
co-pilot needs to be frighteningly fast
00:12:07.680 --> 00:12:10.839
because it needs to move at the speed
00:12:09.240 --> 00:12:12.880
that like programmers are thinking in
00:12:10.839 --> 00:12:14.920
programming next whereas something like
00:12:12.880 --> 00:12:16.800
Claud it doesn't you know using it in
00:12:14.920 --> 00:12:18.880
the way that I use cloud here doesn't
00:12:16.800 --> 00:12:22.600
really matter because I can say uh
00:12:18.880 --> 00:12:24.079
programing me a you know a web app and
00:12:22.600 --> 00:12:25.360
then I can go and have dinner and come
00:12:24.079 --> 00:12:28.199
back and have a web app and I'd be
00:12:25.360 --> 00:12:31.720
perfectly happy with that right so um
00:12:28.199 --> 00:12:37.199
the latency request are also
00:12:31.720 --> 00:12:37.199
different cool um any any questions here
00:12:37.399 --> 00:12:42.600
yeah that debugging code they
00:12:43.000 --> 00:12:47.959
are the well so
00:12:45.839 --> 00:12:50.760
co-pilot I haven't actually tried it
00:12:47.959 --> 00:12:52.480
that much um if I wanted to debug code
00:12:50.760 --> 00:12:54.880
I'd probably use something like pla or
00:12:52.480 --> 00:12:56.360
gp4 just because actually I'll I'll
00:12:54.880 --> 00:12:58.320
mention this in a second but co-pilot's
00:12:56.360 --> 00:13:00.360
a much smaller model uh because it needs
00:12:58.320 --> 00:13:01.839
to be very fast or what they're using in
00:13:00.360 --> 00:13:04.040
copilot is a smaller model because it
00:13:01.839 --> 00:13:05.519
needs to be very fast so I would
00:13:04.040 --> 00:13:08.360
probably use a bigger model for anything
00:13:05.519 --> 00:13:10.120
that required like good understanding I
00:13:08.360 --> 00:13:11.480
think it's passable at debugging code
00:13:10.120 --> 00:13:13.079
but it won't find the really difficult
00:13:11.480 --> 00:13:15.639
things and it probably won't find things
00:13:13.079 --> 00:13:18.279
that require spanning across uh multiple
00:13:15.639 --> 00:13:21.240
files but I I'm not 100% sure about that
00:13:18.279 --> 00:13:25.519
like I think it's worth
00:13:21.240 --> 00:13:25.519
testing um any other
00:13:25.880 --> 00:13:30.120
questions okay so if I haven't convinced
00:13:28.360 --> 00:13:32.360
you that as software developers you
00:13:30.120 --> 00:13:34.880
should be using this hopefully this next
00:13:32.360 --> 00:13:37.480
uh this next slide will so this was a
00:13:34.880 --> 00:13:41.199
study that was run by GitHub uh shortly
00:13:37.480 --> 00:13:43.160
after um after co-pilot came out and so
00:13:41.199 --> 00:13:45.440
why do we do code generation why are
00:13:43.160 --> 00:13:47.240
people very excited about it so the
00:13:45.440 --> 00:13:50.240
first is U making software isn't
00:13:47.240 --> 00:13:53.480
important um and I recently calculated
00:13:50.240 --> 00:13:55.920
what from some Labor Statistics and the
00:13:53.480 --> 00:13:59.440
total amount that software developers
00:13:55.920 --> 00:14:01.880
make um in a year is $175 billion so
00:13:59.440 --> 00:14:05.000
that's providing at least that much you
00:14:01.880 --> 00:14:06.800
know value so it's a very high value uh
00:14:05.000 --> 00:14:09.079
profession so if we could make it faster
00:14:06.800 --> 00:14:11.480
you know it would have even more
00:14:09.079 --> 00:14:12.920
value another thing is code generation
00:14:11.480 --> 00:14:15.680
leads to large improvements in
00:14:12.920 --> 00:14:17.160
productivity so uh get Hub ran this
00:14:15.680 --> 00:14:18.680
study where they randomly assigned
00:14:17.160 --> 00:14:21.519
developers to groups who would either
00:14:18.680 --> 00:14:24.440
use co-pilot or not use co-pilot and
00:14:21.519 --> 00:14:26.480
they assigned them the same task and
00:14:24.440 --> 00:14:30.759
basically the people who use copilot
00:14:26.480 --> 00:14:34.199
their rate of um completion went up by
00:14:30.759 --> 00:14:36.320
8% and they finished um in about 40% of
00:14:34.199 --> 00:14:39.279
the time of the people who didn't use it
00:14:36.320 --> 00:14:43.639
and so I think this
00:14:39.279 --> 00:14:45.920
is or uh yeah they say 55% less times so
00:14:43.639 --> 00:14:47.759
this is very impressive but it's also
00:14:45.920 --> 00:14:50.199
not at all surprising if you're using a
00:14:47.759 --> 00:14:52.880
Cod like assisted coding assistant it
00:14:50.199 --> 00:14:54.360
just makes you code faster also if you
00:14:52.880 --> 00:14:56.040
don't like writing doc strings it's
00:14:54.360 --> 00:14:57.519
really good at writing doc strings so
00:14:56.040 --> 00:14:59.680
you can write documentation for your
00:14:57.519 --> 00:15:00.759
code not wor about so
00:14:59.680 --> 00:15:04.399
okay
00:15:00.759 --> 00:15:07.000
cool um
00:15:04.399 --> 00:15:09.720
so there are differences between code
00:15:07.000 --> 00:15:14.000
and natural language uh and I've listed
00:15:09.720 --> 00:15:15.560
a few of them here and the differences
00:15:14.000 --> 00:15:18.120
between code and natural language also
00:15:15.560 --> 00:15:20.160
affect how we build models for this test
00:15:18.120 --> 00:15:23.160
so the first one is that code has strict
00:15:20.160 --> 00:15:26.000
grammar uh if you make a small mistake
00:15:23.160 --> 00:15:27.920
in your code grammar usually it will
00:15:26.000 --> 00:15:29.839
just break and your program won't work
00:15:27.920 --> 00:15:31.319
so you need to be very careful as
00:15:29.839 --> 00:15:32.560
opposed to natural language grammar
00:15:31.319 --> 00:15:33.600
where you can make small mistakes and it
00:15:32.560 --> 00:15:36.120
doesn't make a
00:15:33.600 --> 00:15:40.120
difference another thing is in code you
00:15:36.120 --> 00:15:42.720
know the semantic flow of the code and
00:15:40.120 --> 00:15:44.160
so we know that certain variables
00:15:42.720 --> 00:15:45.560
correspond to each other we know that
00:15:44.160 --> 00:15:48.639
they're flowing through the program in a
00:15:45.560 --> 00:15:50.880
certain way another thing is code is
00:15:48.639 --> 00:15:54.120
executable so we can actually execute it
00:15:50.880 --> 00:15:56.199
and observe the result unlike in natural
00:15:54.120 --> 00:16:00.000
language and another important thing is
00:15:56.199 --> 00:16:03.399
code is created incrementally so code is
00:16:00.000 --> 00:16:05.680
not you know unlike text text is also
00:16:03.399 --> 00:16:07.399
created incrementally but it's not
00:16:05.680 --> 00:16:08.720
usually you write it once you might
00:16:07.399 --> 00:16:11.199
revise it a little bit and then you're
00:16:08.720 --> 00:16:14.040
done and you you don't need to touch it
00:16:11.199 --> 00:16:15.399
again but um in code you touch it over
00:16:14.040 --> 00:16:17.800
and over and over again as you develop a
00:16:15.399 --> 00:16:17.800
sof
00:16:18.040 --> 00:16:23.040
project so if we look at code Generation
00:16:21.079 --> 00:16:27.079
Um I would like to talk a little bit
00:16:23.040 --> 00:16:29.079
about uh subtasks and data sets next so
00:16:27.079 --> 00:16:30.480
the most famous data set for a Cod code
00:16:29.079 --> 00:16:34.279
generation nowadays is something called
00:16:30.480 --> 00:16:38.680
human ofel um this is a very nice data
00:16:34.279 --> 00:16:42.480
set um for a number of reasons uh I
00:16:38.680 --> 00:16:44.240
think it is used too much um nonetheless
00:16:42.480 --> 00:16:46.759
and I I think there are better data sets
00:16:44.240 --> 00:16:51.240
that we maybe should be using more but
00:16:46.759 --> 00:16:54.000
basically human ofel is um it has
00:16:51.240 --> 00:16:55.920
examples of usage of the Python standard
00:16:54.000 --> 00:16:59.360
Library where some are easier some are
00:16:55.920 --> 00:17:02.880
harder and just to give some examples
00:16:59.360 --> 00:17:06.760
uh we're saying given a nonempty list of
00:17:02.880 --> 00:17:10.480
integers return the sum of all the odd
00:17:06.760 --> 00:17:12.959
elements that are in even positions so
00:17:10.480 --> 00:17:16.079
it's kind of like a elite code
00:17:12.959 --> 00:17:19.199
style you know program but maybe one of
00:17:16.079 --> 00:17:22.400
the easier ones and then in order to
00:17:19.199 --> 00:17:25.240
solve that you find all of the put
00:17:22.400 --> 00:17:28.480
elements in even positions and then you
00:17:25.240 --> 00:17:29.679
only return them if uh the value itself
00:17:28.480 --> 00:17:32.799
is
00:17:29.679 --> 00:17:34.200
um so like you can do that in a oneliner
00:17:32.799 --> 00:17:36.600
but you need to think about it a little
00:17:34.200 --> 00:17:38.919
bit um and then you have
00:17:36.600 --> 00:17:43.120
more
00:17:38.919 --> 00:17:43.810
um returns encoded uh sorry takes an
00:17:43.120 --> 00:17:46.910
input
00:17:43.810 --> 00:17:46.910
[Music]
00:17:47.160 --> 00:17:50.919
string yeah actually sorry this is from
00:17:49.320 --> 00:17:53.600
the paper I didn't read it before I copy
00:17:50.919 --> 00:17:57.080
pasted it in here but um yeah that's a
00:17:53.600 --> 00:17:58.880
decoding one and one one thing about
00:17:57.080 --> 00:18:02.240
this uh that's important to know is it
00:17:58.880 --> 00:18:04.200
only has 164 examples so it's actually a
00:18:02.240 --> 00:18:07.600
relatively small number of
00:18:04.200 --> 00:18:09.440
examples um it's also just the python
00:18:07.600 --> 00:18:11.200
standard Library so it's not testing
00:18:09.440 --> 00:18:14.960
usage of any other
00:18:11.200 --> 00:18:17.520
libraries um so these two things
00:18:14.960 --> 00:18:19.720
together make it not the most realistic
00:18:17.520 --> 00:18:21.880
you know examination of your programming
00:18:19.720 --> 00:18:23.640
skills just like leak code is not the
00:18:21.880 --> 00:18:25.640
most realistic examination of your
00:18:23.640 --> 00:18:28.240
programming skills but you know I don't
00:18:25.640 --> 00:18:31.720
know companies use it anyway so maybe
00:18:28.240 --> 00:18:35.159
human devel is reasonable but um so then
00:18:31.720 --> 00:18:37.120
we go um into the inputs and outputs uh
00:18:35.159 --> 00:18:40.679
the inputs and outputs usually include a
00:18:37.120 --> 00:18:43.440
doc string um some input and output
00:18:40.679 --> 00:18:47.640
examples and then they have tests to
00:18:43.440 --> 00:18:47.640
verify the accuracy of your
00:18:47.880 --> 00:18:52.840
outputs so the metric that's used to
00:18:50.559 --> 00:18:58.919
evaluate these systems is something
00:18:52.840 --> 00:19:01.400
called passet K and the basic idea is um
00:18:58.919 --> 00:19:03.400
we generate K examples will at least one
00:19:01.400 --> 00:19:06.960
of them pass the unit
00:19:03.400 --> 00:19:10.720
tests and the idea here is
00:19:06.960 --> 00:19:13.480
that if we have models we might want to
00:19:10.720 --> 00:19:14.960
generate like well there there's a
00:19:13.480 --> 00:19:17.480
couple reasons why we would care about
00:19:14.960 --> 00:19:19.880
this pass it one is kind of obvious
00:19:17.480 --> 00:19:23.200
because we generate one and then we
00:19:19.880 --> 00:19:26.480
measure how um you know how likely it is
00:19:23.200 --> 00:19:29.280
to pass unit tests but pass it five why
00:19:26.480 --> 00:19:30.760
would we care about passet five well
00:19:29.280 --> 00:19:32.159
number one maybe you could show five
00:19:30.760 --> 00:19:34.240
programs to a person and they could
00:19:32.159 --> 00:19:37.039
choose the one that they like the best
00:19:34.240 --> 00:19:39.919
or maybe you could have unit test write
00:19:37.039 --> 00:19:41.720
unit tests in advance and then generate
00:19:39.919 --> 00:19:43.880
five programs check which one pass the
00:19:41.720 --> 00:19:45.480
unit tests and then use the ones only
00:19:43.880 --> 00:19:48.360
that pass the unit test or something
00:19:45.480 --> 00:19:51.000
like that so there's also some interest
00:19:48.360 --> 00:19:53.320
in uh whether you could generate you
00:19:51.000 --> 00:19:54.600
know multiple examples and then pick a
00:19:53.320 --> 00:19:56.919
good
00:19:54.600 --> 00:19:59.080
one there's a little bit of nuance in
00:19:56.919 --> 00:20:02.120
how this is actually calculated so
00:19:59.080 --> 00:20:04.240
basically um if you generate only K like
00:20:02.120 --> 00:20:05.960
if you if you sample only one example
00:20:04.240 --> 00:20:07.400
there's a lot of variance in whether you
00:20:05.960 --> 00:20:10.159
get it right or not so what they
00:20:07.400 --> 00:20:13.440
actually do is they generate like 10
00:20:10.159 --> 00:20:15.600
outputs or 200 outputs and then they
00:20:13.440 --> 00:20:18.159
calculate the expected number of those
00:20:15.600 --> 00:20:20.320
that the expected number of cases where
00:20:18.159 --> 00:20:23.280
that would pass by just doing a little
00:20:20.320 --> 00:20:25.440
bit of uh like math calculating the
00:20:23.280 --> 00:20:28.679
number of combinations where one passes
00:20:25.440 --> 00:20:30.720
or one doesn't and here k n is the total
00:20:28.679 --> 00:20:34.240
number you generate C is the number of
00:20:30.720 --> 00:20:36.520
correct ansers and K is uh your passive
00:20:34.240 --> 00:20:36.520
K
00:20:37.159 --> 00:20:43.360
value
00:20:38.919 --> 00:20:46.280
cool um so any any questions about
00:20:43.360 --> 00:20:47.880
these you'll you'll see a bunch of uh
00:20:46.280 --> 00:20:50.520
people evaluating on this human ofel
00:20:47.880 --> 00:20:52.760
with passive K including all of the you
00:20:50.520 --> 00:20:57.520
know new llms that come out it's a very
00:20:52.760 --> 00:20:57.520
standard Edge yeah
00:21:01.760 --> 00:21:06.039
is yeah that that's a good um question I
00:21:04.919 --> 00:21:07.840
think I'm going to cover that a little
00:21:06.039 --> 00:21:11.039
bit later but I might as well say it now
00:21:07.840 --> 00:21:13.640
so llms
00:21:11.039 --> 00:21:15.080
are llms are good at code because they
00:21:13.640 --> 00:21:16.880
intentionally include a lot of code
00:21:15.080 --> 00:21:19.520
training data in LL training and the
00:21:16.880 --> 00:21:22.679
reason for that is twofold um the first
00:21:19.520 --> 00:21:25.320
one is that code generation is a huge
00:21:22.679 --> 00:21:26.960
application of llms right now and like
00:21:25.320 --> 00:21:28.679
if you had an llm that couldn't do code
00:21:26.960 --> 00:21:32.320
generation it'd be kind of embarrassing
00:21:28.679 --> 00:21:33.960
so um Everybody includes this number two
00:21:32.320 --> 00:21:36.600
uh code has been shown to improve kind
00:21:33.960 --> 00:21:38.080
of the reasoning abilities of llms and
00:21:36.600 --> 00:21:41.640
because of that people include code for
00:21:38.080 --> 00:21:43.440
that purpose so yeah um it's not that
00:21:41.640 --> 00:21:45.600
LMS are inherently good at code or
00:21:43.440 --> 00:21:48.840
anything it's that they have lots of
00:21:45.600 --> 00:21:51.640
lots of code TR and I'll I'll explain
00:21:48.840 --> 00:21:54.279
exactly how they construct this
00:21:51.640 --> 00:21:57.200
St and actually if you remember last
00:21:54.279 --> 00:21:59.640
time uh I talked about the pile which
00:21:57.200 --> 00:22:01.039
was or not last time but uh when I
00:21:59.640 --> 00:22:03.159
talked about the tour of large language
00:22:01.039 --> 00:22:06.360
models I talked about the pile and the
00:22:03.159 --> 00:22:09.799
pile is almost half toe for
00:22:06.360 --> 00:22:12.000
example cool any other
00:22:09.799 --> 00:22:17.240
questions
00:22:12.000 --> 00:22:19.320
okay so another uh a first Improvement
00:22:17.240 --> 00:22:22.080
or at least change that we can make to
00:22:19.320 --> 00:22:23.880
human ofel is uh going to broader
00:22:22.080 --> 00:22:26.720
domains and covering a broader variety
00:22:23.880 --> 00:22:28.559
of libraries and this is a data set that
00:22:26.720 --> 00:22:30.880
we created actually a long time ago but
00:22:28.559 --> 00:22:33.799
but we recently added execution based
00:22:30.880 --> 00:22:36.159
evaluation to it it's called konola and
00:22:33.799 --> 00:22:36.919
the execution based uh evaluation one is
00:22:36.159 --> 00:22:40.360
called
00:22:36.919 --> 00:22:43.039
odex and basically what we did here is
00:22:40.360 --> 00:22:45.720
we scraped data from stack Overflow
00:22:43.039 --> 00:22:48.039
including uh inputs and output uh
00:22:45.720 --> 00:22:50.559
Solutions and then based on this scraped
00:22:48.039 --> 00:22:54.240
data we uh did some manual curation to
00:22:50.559 --> 00:22:57.640
turn these into like actual questions um
00:22:54.240 --> 00:22:59.640
and answers about how you could write uh
00:22:57.640 --> 00:23:01.799
solve programming
00:22:59.640 --> 00:23:04.080
problems and
00:23:01.799 --> 00:23:05.600
um because this is scraped from stack
00:23:04.080 --> 00:23:09.159
Overflow there's no restriction that
00:23:05.600 --> 00:23:10.520
this is from the python standard Library
00:23:09.159 --> 00:23:13.200
which also means that it can cover a
00:23:10.520 --> 00:23:14.919
very wide variety of libraries and it's
00:23:13.200 --> 00:23:16.760
approximately according to the
00:23:14.919 --> 00:23:20.320
popularity of the libraries because we
00:23:16.760 --> 00:23:24.159
took popular posts so um that's a a good
00:23:20.320 --> 00:23:25.400
thing uh you know it it is a reasonable
00:23:24.159 --> 00:23:26.559
way to come up with a realistic
00:23:25.400 --> 00:23:29.520
distribution of libraries that you
00:23:26.559 --> 00:23:31.799
should be looking at um odex adds
00:23:29.520 --> 00:23:34.159
execution based evaluation previously
00:23:31.799 --> 00:23:36.679
what we had was we only had the snippet
00:23:34.159 --> 00:23:40.600
that was able to solve the problem as
00:23:36.679 --> 00:23:42.360
opposed to um as opposed to being able
00:23:40.600 --> 00:23:46.880
to execute unit
00:23:42.360 --> 00:23:49.440
tests and just to show how this has a
00:23:46.880 --> 00:23:52.000
broader variety of libraries on the top
00:23:49.440 --> 00:23:53.919
we have the distribution of odex
00:23:52.000 --> 00:23:57.320
libraries and we can see about half of
00:23:53.919 --> 00:23:59.600
them use libraries and this includes a
00:23:57.320 --> 00:24:01.279
variety of things including pandas
00:23:59.600 --> 00:24:04.799
numpy
00:24:01.279 --> 00:24:06.400
um reg o selections you know all of
00:24:04.799 --> 00:24:09.279
these should be libraries that look
00:24:06.400 --> 00:24:14.559
familiar to you um in contrast if we
00:24:09.279 --> 00:24:17.200
look at human eval human eval is right
00:24:14.559 --> 00:24:18.840
here so you can see almost all of the
00:24:17.200 --> 00:24:20.600
questions require no libraries and all
00:24:18.840 --> 00:24:22.120
of the other ones require libraries that
00:24:20.600 --> 00:24:24.360
were included in the pipe onstead
00:24:22.120 --> 00:24:27.640
libraries so
00:24:24.360 --> 00:24:29.120
um in reality this is probably more what
00:24:27.640 --> 00:24:30.120
your program in queries are going to
00:24:29.120 --> 00:24:31.240
look like they're not going to look like
00:24:30.120 --> 00:24:33.600
lead code they're going to look like
00:24:31.240 --> 00:24:33.600
using
00:24:35.360 --> 00:24:42.080
APS so um originally when we did conal
00:24:40.039 --> 00:24:44.200
we didn't use execution based evaluation
00:24:42.080 --> 00:24:47.480
because creating unit tests uh for lots
00:24:44.200 --> 00:24:51.360
of stack Overflow posts is hard
00:24:47.480 --> 00:24:53.640
um specifically there's two issues the
00:24:51.360 --> 00:24:55.000
first one is that it requires that code
00:24:53.640 --> 00:24:58.880
be easily
00:24:55.000 --> 00:25:02.320
executable um now think about
00:24:58.880 --> 00:25:04.559
how you would do that for Matt plot lib
00:25:02.320 --> 00:25:06.200
for example how would you create a unit
00:25:04.559 --> 00:25:08.080
test to test whether Matt plot lib
00:25:06.200 --> 00:25:10.760
successfully created a bar chart for
00:25:08.080 --> 00:25:12.440
something it's kind of tough right you
00:25:10.760 --> 00:25:13.840
like you would have to get the image and
00:25:12.440 --> 00:25:16.919
you'd have to confirm that the image was
00:25:13.840 --> 00:25:21.200
a bar chart and uh other things like
00:25:16.919 --> 00:25:22.720
that um even worse what if it was uh
00:25:21.200 --> 00:25:25.600
kind of like a server framework like
00:25:22.720 --> 00:25:27.440
ajango how would you confirm that ajango
00:25:25.600 --> 00:25:30.559
you know server is working appropriately
00:25:27.440 --> 00:25:32.600
and that's kind of tricky so um actually
00:25:30.559 --> 00:25:34.480
coming up with realistic unit tests for
00:25:32.600 --> 00:25:36.919
real programs can be
00:25:34.480 --> 00:25:38.840
difficult um another problem with
00:25:36.919 --> 00:25:41.640
execution based evaluation is it ignores
00:25:38.840 --> 00:25:45.320
stylistic considerations so I could
00:25:41.640 --> 00:25:48.279
write very spaghetti like very spaghetti
00:25:45.320 --> 00:25:50.200
code and as long as it executed properly
00:25:48.279 --> 00:25:52.559
it would still be judged as correct and
00:25:50.200 --> 00:25:54.399
sometimes that's actually an issue so
00:25:52.559 --> 00:25:56.360
usually it's not a problem because
00:25:54.399 --> 00:25:58.600
language models write reasonably good
00:25:56.360 --> 00:26:00.600
code but sometimes you want to match the
00:25:58.600 --> 00:26:05.039
or other things like that
00:26:00.600 --> 00:26:06.559
so some alternatives are blue score
00:26:05.039 --> 00:26:09.000
which we've talked about before it's
00:26:06.559 --> 00:26:12.679
basically count calculating the engram
00:26:09.000 --> 00:26:16.919
overlap between a gold standard human uh
00:26:12.679 --> 00:26:20.440
implementation and a uh in the system
00:26:16.919 --> 00:26:24.000
output and there's also specifically
00:26:20.440 --> 00:26:26.480
adapted methods for evaluating code and
00:26:24.000 --> 00:26:29.080
so there's a method called code blue and
00:26:26.480 --> 00:26:31.360
basically the way code blue works is it
00:26:29.080 --> 00:26:35.240
also considers the syntax and semantic
00:26:31.360 --> 00:26:37.080
flow of the code so it measures overlap
00:26:35.240 --> 00:26:40.120
between
00:26:37.080 --> 00:26:42.120
strings in the original code but it also
00:26:40.120 --> 00:26:48.640
considers overlap between the syntax
00:26:42.120 --> 00:26:53.000
trees of the code and uh whether the
00:26:48.640 --> 00:26:56.320
um these like semantic information flow
00:26:53.000 --> 00:26:57.919
graphs look similar so uh all all of
00:26:56.320 --> 00:26:59.440
these things work together to calculate
00:26:57.919 --> 00:27:02.720
the C
00:26:59.440 --> 00:27:04.480
St one thing I I should mention is how
00:27:02.720 --> 00:27:06.840
do we get these syntax trees in the
00:27:04.480 --> 00:27:09.039
first place um for example if we're
00:27:06.840 --> 00:27:12.919
talking about python there's a python
00:27:09.039 --> 00:27:14.760
Library uh for ab abstract syntax tree
00:27:12.919 --> 00:27:16.559
it's just part of the standard library
00:27:14.760 --> 00:27:18.320
and it's necessary to run the python
00:27:16.559 --> 00:27:20.559
interpreter so you can just get these
00:27:18.320 --> 00:27:24.320
trees directly from the python ASD
00:27:20.559 --> 00:27:25.880
Library uh not hard to do uh for this I
00:27:24.320 --> 00:27:27.840
forget what they did in the code blue
00:27:25.880 --> 00:27:30.679
thing but there are uh analyzers that
00:27:27.840 --> 00:27:32.120
allow you to analyze this control FL so
00:27:30.679 --> 00:27:34.159
this is taking advantage of the fact
00:27:32.120 --> 00:27:37.440
that code is you know predictable it has
00:27:34.159 --> 00:27:41.480
predictable syntax and you can you
00:27:37.440 --> 00:27:43.960
can6 um one disadvantage of blue and
00:27:41.480 --> 00:27:45.799
code blue of course is that you know you
00:27:43.960 --> 00:27:47.679
can write two very different looking
00:27:45.799 --> 00:27:49.559
programs that actually are both correct
00:27:47.679 --> 00:27:51.799
and blue will underestimate the goodness
00:27:49.559 --> 00:27:54.440
of those programs so maybe using both of
00:27:51.799 --> 00:27:57.159
them together is uh is
00:27:54.440 --> 00:28:00.120
appropriate uh if if you can write unit
00:27:57.159 --> 00:28:00.120
Test please
00:28:00.559 --> 00:28:04.279
um another one which I'll just cover
00:28:02.600 --> 00:28:05.399
very briefly we talked about BT score
00:28:04.279 --> 00:28:08.159
before when I was talking about
00:28:05.399 --> 00:28:11.120
evaluation of uh you know generated text
00:28:08.159 --> 00:28:13.480
and there's also code BT score which um
00:28:11.120 --> 00:28:15.799
we uh we created here at
00:28:13.480 --> 00:28:20.080
CMU and it's basically an embedding
00:28:15.799 --> 00:28:21.760
based metric uh to compare code and so
00:28:20.080 --> 00:28:23.399
Bert score if you remember basically
00:28:21.760 --> 00:28:25.679
what it did is it calculated the coign
00:28:23.399 --> 00:28:27.840
similarity between each of the tokens uh
00:28:25.679 --> 00:28:30.159
between a generated text and a reference
00:28:27.840 --> 00:28:34.279
text we do exactly the same thing for
00:28:30.159 --> 00:28:36.080
code um so we calculate the Sim cosine
00:28:34.279 --> 00:28:39.200
similarity between tokens for a
00:28:36.080 --> 00:28:42.960
reference code and generated
00:28:39.200 --> 00:28:45.000
code and we released a model called
00:28:42.960 --> 00:28:46.559
codir which was basically Bert but
00:28:45.000 --> 00:28:49.440
continued trained on lots and lots of
00:28:46.559 --> 00:28:51.840
code uh that allowed us to do that and
00:28:49.440 --> 00:28:55.480
um basically we were able to demonstrate
00:28:51.840 --> 00:28:59.200
that this gave better correlation both
00:28:55.480 --> 00:29:01.480
with final execution accuracy and with
00:28:59.200 --> 00:29:05.200
human judgments of whether the the code
00:29:01.480 --> 00:29:08.000
was correct and so um some people uh
00:29:05.200 --> 00:29:09.559
created a data set of human correctness
00:29:08.000 --> 00:29:12.559
judgments and we were able to put a
00:29:09.559 --> 00:29:14.240
little better with that as well um why
00:29:12.559 --> 00:29:15.640
do we care about correlation with
00:29:14.240 --> 00:29:17.399
execution
00:29:15.640 --> 00:29:20.200
accuracy
00:29:17.399 --> 00:29:22.320
um this is important in the cases when
00:29:20.200 --> 00:29:23.559
we can't create unit tests or when
00:29:22.320 --> 00:29:26.120
creating unit test would be too
00:29:23.559 --> 00:29:27.519
expensive so this gives us a better
00:29:26.120 --> 00:29:30.640
approximation for what we would get if
00:29:27.519 --> 00:29:30.640
we ran tests
00:29:39.840 --> 00:29:45.000
in yeah so we did not we did not
00:29:42.600 --> 00:29:46.799
consider code structure here uh would
00:29:45.000 --> 00:29:48.480
different variable names affect it yes
00:29:46.799 --> 00:29:50.159
different variable names would affect it
00:29:48.480 --> 00:29:51.799
but not as much as the other metrics
00:29:50.159 --> 00:29:53.960
which is why it's better why it has
00:29:51.799 --> 00:29:56.720
better
00:29:53.960 --> 00:30:00.000
correlations and like for example
00:29:56.720 --> 00:30:03.679
codir I imagine probably gives very
00:30:00.000 --> 00:30:05.120
similar representations to I and J just
00:30:03.679 --> 00:30:07.960
because they're both used in iterators
00:30:05.120 --> 00:30:09.039
all the time whereas uh a normal Burt
00:30:07.960 --> 00:30:10.960
model would give very different
00:30:09.039 --> 00:30:12.760
representations to I and J right because
00:30:10.960 --> 00:30:14.960
I is like a personal pronoun and J is
00:30:12.760 --> 00:30:17.200
not so um that's the reason why
00:30:14.960 --> 00:30:20.399
continued training would
00:30:17.200 --> 00:30:24.799
help cool any other
00:30:20.399 --> 00:30:26.640
things okay so another um another place
00:30:24.799 --> 00:30:29.480
where code generation can be useful uh
00:30:26.640 --> 00:30:33.440
we had the example of collab uh is in
00:30:29.480 --> 00:30:36.200
collab notebooks and this or in uh data
00:30:33.440 --> 00:30:38.519
science notebooks this paper was by uh
00:30:36.200 --> 00:30:41.440
Google so this might actually even be
00:30:38.519 --> 00:30:43.960
used in the collab thing because collab
00:30:41.440 --> 00:30:45.640
is a Google thing um but data data
00:30:43.960 --> 00:30:47.320
science notebooks allow for incremental
00:30:45.640 --> 00:30:50.519
implementation I'm sure a lot of people
00:30:47.320 --> 00:30:53.559
here or almost everybody here uses them
00:30:50.519 --> 00:30:55.279
um and another interesting thing is say
00:30:53.559 --> 00:30:57.519
allow for evaluation of code generation
00:30:55.279 --> 00:30:58.960
in context uh or incremental code
00:30:57.519 --> 00:31:00.639
generation
00:30:58.960 --> 00:31:02.720
and so you start out with like a
00:31:00.639 --> 00:31:04.880
notebook and then you have AAL
00:31:02.720 --> 00:31:06.600
languageand and then youate the output
00:31:04.880 --> 00:31:09.240
AAL language command you generate the
00:31:06.600 --> 00:31:10.799
output etc etc so this is an extal
00:31:09.240 --> 00:31:14.519
example from the STA
00:31:10.799 --> 00:31:17.519
set um so this paper is very nice it it
00:31:14.519 --> 00:31:20.320
has a lot of uh you know it's a nice
00:31:17.519 --> 00:31:21.720
data set one other thing that was really
00:31:20.320 --> 00:31:24.200
interesting from this paper is it
00:31:21.720 --> 00:31:27.919
demonstrated the problem of data leakage
00:31:24.200 --> 00:31:29.679
in evaluating models and this is a Rel
00:31:27.919 --> 00:31:32.440
relatively large problem I don't know if
00:31:29.679 --> 00:31:33.799
we have a silver bullet solution for
00:31:32.440 --> 00:31:36.120
this but it's an important thing to be
00:31:33.799 --> 00:31:38.120
aware of uh not just for code generation
00:31:36.120 --> 00:31:39.639
but these are examples from code
00:31:38.120 --> 00:31:43.519
generation
00:31:39.639 --> 00:31:45.679
so here um in the arcade data set they
00:31:43.519 --> 00:31:48.519
basically both evaluated existing
00:31:45.679 --> 00:31:51.720
notebooks and they evaluated notebooks
00:31:48.519 --> 00:31:53.279
that um existing notebooks that they got
00:31:51.720 --> 00:31:55.960
from the web and they evaluated
00:31:53.279 --> 00:31:59.000
notebooks that they actually created
00:31:55.960 --> 00:32:00.399
themselves and there's very very Stark
00:31:59.000 --> 00:32:02.600
difference between the notebooks that
00:32:00.399 --> 00:32:04.440
were created on the web and the
00:32:02.600 --> 00:32:07.399
notebooks that they evaluated themselves
00:32:04.440 --> 00:32:10.159
so like most of the code generation
00:32:07.399 --> 00:32:11.679
models except for Palm uh which was the
00:32:10.159 --> 00:32:14.760
best model when they created this data
00:32:11.679 --> 00:32:17.360
set did really poorly or did really well
00:32:14.760 --> 00:32:21.120
on the existing data and quite poorly on
00:32:17.360 --> 00:32:25.279
the new data um which is probably an
00:32:21.120 --> 00:32:28.159
indication of um probably an indication
00:32:25.279 --> 00:32:29.720
of the fact that you know this is to
00:32:28.159 --> 00:32:32.240
some extent leaked into the training
00:32:29.720 --> 00:32:35.320
data of the language models there was
00:32:32.240 --> 00:32:37.760
also a very recent
00:32:35.320 --> 00:32:40.240
um paper actually I think this might be
00:32:37.760 --> 00:32:43.159
2024 there was a very recent paper that
00:32:40.240 --> 00:32:45.880
did a similar thing uh where they
00:32:43.159 --> 00:32:48.440
evaluated on human ofel and then their
00:32:45.880 --> 00:32:52.000
live codebench in live codebench
00:32:48.440 --> 00:32:55.639
basically what they did is they tried to
00:32:52.000 --> 00:32:58.519
pick problems from Le code and other
00:32:55.639 --> 00:33:00.519
websites that were more recent versus
00:32:58.519 --> 00:33:01.960
less recent and they have some really
00:33:00.519 --> 00:33:04.880
nice graphs in their paper where they
00:33:01.960 --> 00:33:06.519
demonstrate that the less recent ones
00:33:04.880 --> 00:33:08.159
before the training cut off have like a
00:33:06.519 --> 00:33:10.080
high accuracy and then suddenly it drops
00:33:08.159 --> 00:33:12.639
right at the trading C off of the the
00:33:10.080 --> 00:33:13.480
models so this is something to to be
00:33:12.639 --> 00:33:17.360
aware
00:33:13.480 --> 00:33:20.519
of and what this figure is showing here
00:33:17.360 --> 00:33:24.039
is this figure is showing on the xaxis
00:33:20.519 --> 00:33:26.840
pass it one on the Live code bench easy
00:33:24.039 --> 00:33:28.679
and then pass it one on human ofel so we
00:33:26.840 --> 00:33:31.480
see this kn
00:33:28.679 --> 00:33:34.039
correlation between
00:33:31.480 --> 00:33:35.919
essentially like passing on life code
00:33:34.039 --> 00:33:37.399
bench easy and passing on human ofel
00:33:35.919 --> 00:33:40.000
then we have this group of models that
00:33:37.399 --> 00:33:42.159
are kind of like up here and these are
00:33:40.000 --> 00:33:43.960
ones where basically it's likely that
00:33:42.159 --> 00:33:46.480
human ofel leaked into the training data
00:33:43.960 --> 00:33:48.840
because they're getting better scores on
00:33:46.480 --> 00:33:50.919
human ofel than you would expect that
00:33:48.840 --> 00:33:53.360
they get uh you know just looking at
00:33:50.919 --> 00:33:55.360
their uh you know performance on another
00:33:53.360 --> 00:33:57.320
data set there's also a nice like
00:33:55.360 --> 00:34:00.000
analogous one for math reasoning
00:33:57.320 --> 00:34:01.519
problems um like this so this is
00:34:00.000 --> 00:34:03.039
definitely something to be aware of if
00:34:01.519 --> 00:34:04.559
you're looking only at like very
00:34:03.039 --> 00:34:06.200
standard benchmarks that people are
00:34:04.559 --> 00:34:11.159
trading
00:34:06.200 --> 00:34:11.159
in cool um any questions about
00:34:12.119 --> 00:34:19.240
this okay um another data set uh that I
00:34:17.720 --> 00:34:20.599
I really like the concept of and
00:34:19.240 --> 00:34:22.919
recently it's gotten a little bit of
00:34:20.599 --> 00:34:25.399
Buzz because it was used in a um an
00:34:22.919 --> 00:34:28.399
evaluation of a new coding assistant
00:34:25.399 --> 00:34:30.480
called Devon but this is um
00:34:28.399 --> 00:34:32.240
something called sbench and it's issues
00:34:30.480 --> 00:34:34.639
from GitHub and code
00:34:32.240 --> 00:34:37.119
bases uh is the input and you want to
00:34:34.639 --> 00:34:39.480
generate a poll request to basically uh
00:34:37.119 --> 00:34:42.919
solve these issues and so your input is
00:34:39.480 --> 00:34:45.800
like data leak in gbdt due to warm start
00:34:42.919 --> 00:34:48.800
this is about non standard then you have
00:34:45.800 --> 00:34:51.159
the code base um it generates a PR for
00:34:48.800 --> 00:34:53.079
you and then it's run through the unit
00:34:51.159 --> 00:34:55.919
tests to see if it passes all the unit
00:34:53.079 --> 00:34:57.160
test post PRS so it's very similar to
00:34:55.919 --> 00:34:59.240
you know what you would be doing in a
00:34:57.160 --> 00:35:01.280
well Main software project you open a
00:34:59.240 --> 00:35:05.240
issue and then you open a poll request
00:35:01.280 --> 00:35:07.800
to fix an issue um this requires things
00:35:05.240 --> 00:35:10.240
like long context understanding um being
00:35:07.800 --> 00:35:13.200
able to do very precise implementations
00:35:10.240 --> 00:35:14.720
based on large software projects and
00:35:13.200 --> 00:35:17.920
right now the state-of-the-art on this
00:35:14.720 --> 00:35:20.680
is at about 14% so it's definitely not a
00:35:17.920 --> 00:35:23.119
solv problem at all um in the original
00:35:20.680 --> 00:35:27.920
paper uh the the state-of-the-art method
00:35:23.119 --> 00:35:29.400
was like 6% or something like that so um
00:35:27.920 --> 00:35:32.079
I imagine that we're not going to get up
00:35:29.400 --> 00:35:33.880
to 90% anytime soon because it's
00:35:32.079 --> 00:35:35.720
probably solving the easier ones and the
00:35:33.880 --> 00:35:37.280
harder ones are you know far beyond the
00:35:35.720 --> 00:35:39.920
ability of any language model we have at
00:35:37.280 --> 00:35:42.320
the moment um but I I really like this
00:35:39.920 --> 00:35:43.960
Benchmark one caveat if you really like
00:35:42.320 --> 00:35:45.520
this Benchmark is that it's kind of
00:35:43.960 --> 00:35:47.760
heavy to run so you need to be a little
00:35:45.520 --> 00:35:51.000
bit careful uh because you need to pull
00:35:47.760 --> 00:35:54.280
in like full repositories to um to run
00:35:51.000 --> 00:35:56.319
on so yeah be a little
00:35:54.280 --> 00:35:57.920
bit sorry there's so many like
00:35:56.319 --> 00:35:59.640
interesting data sets recently in this
00:35:57.920 --> 00:36:01.079
area that I I spent a lot of time on
00:35:59.640 --> 00:36:04.240
data set so I'll try to go a little bit
00:36:01.079 --> 00:36:06.200
more quickly but um uh a final one is
00:36:04.240 --> 00:36:09.359
design to code and this is also a very
00:36:06.200 --> 00:36:11.520
recent data set um basically the idea is
00:36:09.359 --> 00:36:16.359
code generation from websites so your
00:36:11.520 --> 00:36:18.119
input is a website and your output is uh
00:36:16.359 --> 00:36:22.520
like JavaScript code that implements
00:36:18.119 --> 00:36:24.960
that website and or or css or HTML code
00:36:22.520 --> 00:36:26.880
that implements the website so I I
00:36:24.960 --> 00:36:30.119
really like this because you know it's a
00:36:26.880 --> 00:36:32.280
good test bed for multi modal models and
00:36:30.119 --> 00:36:34.040
there aren't a whole lot of strong open
00:36:32.280 --> 00:36:36.160
source multimodal models that can solve
00:36:34.040 --> 00:36:36.960
this at the moment so I think it's kind
00:36:36.160 --> 00:36:39.720
of
00:36:36.960 --> 00:36:41.480
cool um they also proposed a design to
00:36:39.720 --> 00:36:43.480
code model that does the best on this
00:36:41.480 --> 00:36:47.119
data set out of uh you know any of the
00:36:43.480 --> 00:36:47.119
open source models but it's still far
00:36:47.400 --> 00:36:53.040
from and then the question becomes how
00:36:50.680 --> 00:36:56.079
do they um evaluate this in the first
00:36:53.040 --> 00:36:59.440
place and basically the idea is that
00:36:56.079 --> 00:37:01.400
they do highle visual similarity and so
00:36:59.440 --> 00:37:03.920
they calculate visual embeddings of the
00:37:01.400 --> 00:37:06.119
generated sites and then they also do
00:37:03.920 --> 00:37:08.240
lowl element similarity so they try to
00:37:06.119 --> 00:37:10.440
identify all of the elements in the
00:37:08.240 --> 00:37:12.119
generated web page and make sure that uh
00:37:10.440 --> 00:37:15.720
they recall all of the generated
00:37:12.119 --> 00:37:18.760
elements so um I think this is nice one
00:37:15.720 --> 00:37:21.000
thing if you notice um if you use even
00:37:18.760 --> 00:37:25.960
state-ofthe-art like closed models like
00:37:21.000 --> 00:37:28.040
CLA 3 or um GPD 4 is they're really bad
00:37:25.960 --> 00:37:29.440
at this recall they it can generate
00:37:28.040 --> 00:37:31.800
something that looks like maybe a little
00:37:29.440 --> 00:37:33.839
bit similar but it will be missing like
00:37:31.800 --> 00:37:35.720
the elements the design will be off you
00:37:33.839 --> 00:37:37.720
know other stuff like that so I think
00:37:35.720 --> 00:37:41.079
even in the closed like strong models
00:37:37.720 --> 00:37:41.079
this is not a Sol
00:37:41.319 --> 00:37:47.079
problem cool uh
00:37:45.000 --> 00:37:49.880
yeah
00:37:47.079 --> 00:37:51.880
problem um so why is that a hard problem
00:37:49.880 --> 00:37:54.200
for the models I don't actually have a
00:37:51.880 --> 00:37:57.200
really confident answer to that but I
00:37:54.200 --> 00:37:57.200
think
00:38:00.240 --> 00:38:05.200
so one thing I can tell you is that they
00:38:02.839 --> 00:38:08.839
are able to
00:38:05.200 --> 00:38:12.000
improve um so they're able to generate
00:38:08.839 --> 00:38:14.720
something and then I say no that's bad
00:38:12.000 --> 00:38:16.160
please like make it better and it's
00:38:14.720 --> 00:38:17.800
generally better the second time
00:38:16.160 --> 00:38:19.920
especially if you give specific things
00:38:17.800 --> 00:38:22.319
like oh uh but the background on the
00:38:19.920 --> 00:38:25.160
generated site is white but actually it
00:38:22.319 --> 00:38:27.599
should be black and if you think about
00:38:25.160 --> 00:38:31.480
like even a skilled human programmer do
00:38:27.599 --> 00:38:35.119
you think you could write like website
00:38:31.480 --> 00:38:37.680
code and then view it once and then it
00:38:35.119 --> 00:38:40.319
would be correct I think you probably
00:38:37.680 --> 00:38:42.160
couldn't right and so like we're asking
00:38:40.319 --> 00:38:44.040
models to do essentially the same thing
00:38:42.160 --> 00:38:46.920
except they're like even worse than us
00:38:44.040 --> 00:38:48.560
and you know keeping track of all the V
00:38:46.920 --> 00:38:50.720
visual elements and stuff so I think
00:38:48.560 --> 00:38:52.480
it's more like this problem probably
00:38:50.720 --> 00:38:54.720
just needs iterative refinement
00:38:52.480 --> 00:38:58.839
otherwise it's like asking too much of a
00:38:54.720 --> 00:39:02.640
model maybe I don't know
00:38:58.839 --> 00:39:04.520
cool okay so um let's go into methods
00:39:02.640 --> 00:39:06.920
and code generation has some unique
00:39:04.520 --> 00:39:09.400
things um the basic method that you can
00:39:06.920 --> 00:39:11.240
always use is a code generating LM and
00:39:09.400 --> 00:39:13.040
so you feed in previous code or you feed
00:39:11.240 --> 00:39:16.040
in whatever context you have into the LM
00:39:13.040 --> 00:39:18.079
and you generate um uh from it and
00:39:16.040 --> 00:39:20.079
virtually all Serius LMS are trained on
00:39:18.079 --> 00:39:23.079
code nowadays like I I just mentioned
00:39:20.079 --> 00:39:23.079
before
00:39:23.119 --> 00:39:29.920
um one one important thing here is uh
00:39:28.560 --> 00:39:31.240
when you're generating if you're
00:39:29.920 --> 00:39:33.040
generating for something like code
00:39:31.240 --> 00:39:34.480
generation I definitely suggest that you
00:39:33.040 --> 00:39:36.119
modify your temperature settings
00:39:34.480 --> 00:39:38.359
appropriately and set it to a low
00:39:36.119 --> 00:39:42.160
temperature um otherwise you'll get kind
00:39:38.359 --> 00:39:45.079
of crazy uh code but if you set it to a
00:39:42.160 --> 00:39:45.079
low temperature you can get
00:39:46.440 --> 00:39:52.160
better anyway um one really core
00:39:49.640 --> 00:39:54.240
capability of code LMS especially ones
00:39:52.160 --> 00:39:55.599
that you use in your IDE like uh
00:39:54.240 --> 00:39:58.160
co-pilot is
00:39:55.599 --> 00:40:00.000
infilling and um
00:39:58.160 --> 00:40:03.680
the the paper that proposed this is
00:40:00.000 --> 00:40:05.920
actually by Daniel Freed at LTI here and
00:40:03.680 --> 00:40:09.160
um
00:40:05.920 --> 00:40:11.240
the basically what you want to do often
00:40:09.160 --> 00:40:13.000
is you have previous code you have next
00:40:11.240 --> 00:40:14.680
code and you want to just fill in like a
00:40:13.000 --> 00:40:17.960
line that's missing like you want to add
00:40:14.680 --> 00:40:19.040
an extra you know if statement or or
00:40:17.960 --> 00:40:22.720
some sort of
00:40:19.040 --> 00:40:24.880
modification and so the way that at
00:40:22.720 --> 00:40:27.000
least this paper proposed it and the way
00:40:24.880 --> 00:40:29.800
that I think most LMS are actually doing
00:40:27.000 --> 00:40:30.640
this is they take a standard left to
00:40:29.800 --> 00:40:33.200
right
00:40:30.640 --> 00:40:36.040
LM and what they want to do is they want
00:40:33.200 --> 00:40:39.040
to infill this code chunk and so what
00:40:36.040 --> 00:40:40.440
they do is they put a mask in the place
00:40:39.040 --> 00:40:42.119
where they want to fill the chunk which
00:40:40.440 --> 00:40:46.280
would also be where your cursor is in
00:40:42.119 --> 00:40:49.960
your IDE right uh at that point and then
00:40:46.280 --> 00:40:52.680
they have Mas to zero and then at the
00:40:49.960 --> 00:40:57.400
end they put mask to zero again and then
00:40:52.680 --> 00:40:59.000
they output the like you know all of the
00:40:57.400 --> 00:41:01.040
code that you want to generate there and
00:40:59.000 --> 00:41:02.839
so you can just kind of arbitrarily
00:41:01.040 --> 00:41:05.480
generate these trunks by pulling you
00:41:02.839 --> 00:41:07.000
know masking out chunks uh putting in
00:41:05.480 --> 00:41:08.960
The Mask token and then moving it to the
00:41:07.000 --> 00:41:10.440
end of the sequence and then you can
00:41:08.960 --> 00:41:13.160
just use a standard left to right Auto
00:41:10.440 --> 00:41:15.359
regressive language model to solve this
00:41:13.160 --> 00:41:17.040
problem so this is really important if
00:41:15.359 --> 00:41:18.520
you want to build like a co-pilot style
00:41:17.040 --> 00:41:20.160
thing and all of the code language
00:41:18.520 --> 00:41:23.680
models that I talk about at the end of
00:41:20.160 --> 00:41:23.680
this class uh use this
00:41:24.800 --> 00:41:30.440
technique um another thing is there's
00:41:28.160 --> 00:41:33.760
lots of available information uh for
00:41:30.440 --> 00:41:36.040
learning coding things um or for solving
00:41:33.760 --> 00:41:38.880
coding tasks this includes you know the
00:41:36.040 --> 00:41:40.440
current code context of course um also
00:41:38.880 --> 00:41:41.920
the description of the issue that you
00:41:40.440 --> 00:41:45.160
want to be fixing like if you're solving
00:41:41.920 --> 00:41:49.240
a poll request um repo context from
00:41:45.160 --> 00:41:51.880
other files um what tabs you have open
00:41:49.240 --> 00:41:55.920
uh so that that's also an important
00:41:51.880 --> 00:41:58.599
thing and when GitHub co-pilot came out
00:41:55.920 --> 00:42:01.960
they didn't really tell you the details
00:41:58.599 --> 00:42:04.480
of how they were doing this but um
00:42:01.960 --> 00:42:09.079
GitHub co-pilot is written in JavaScript
00:42:04.480 --> 00:42:11.839
and uh there was a p PhD student I think
00:42:09.079 --> 00:42:14.000
from maybe Georgia Tech or something uh
00:42:11.839 --> 00:42:16.839
who or Master student who basically went
00:42:14.000 --> 00:42:19.160
in and took the JavaScript and like Dem
00:42:16.839 --> 00:42:21.839
minified it and like reverse engineered
00:42:19.160 --> 00:42:23.640
what was actually happening um and uh
00:42:21.839 --> 00:42:26.680
wrote A Blog about it and this blog is
00:42:23.640 --> 00:42:28.800
is great uh so basically what uh
00:42:26.680 --> 00:42:32.200
co-pilot was doing which also kind of
00:42:28.800 --> 00:42:33.839
gives you a gold standard um way of uh
00:42:32.200 --> 00:42:36.920
looking
00:42:33.839 --> 00:42:39.440
at uh you know what kind of information
00:42:36.920 --> 00:42:43.440
is necessary to create a good model is
00:42:39.440 --> 00:42:45.240
first they extract um information for
00:42:43.440 --> 00:42:47.400
the prompt given the current document
00:42:45.240 --> 00:42:49.240
and the cursor position so they take the
00:42:47.400 --> 00:42:51.720
current document where is the cursor and
00:42:49.240 --> 00:42:54.640
what is before this and what is after
00:42:51.720 --> 00:42:56.960
this um they identify the relative path
00:42:54.640 --> 00:42:59.960
of the file and what language it's in so
00:42:56.960 --> 00:43:01.760
they they identifi python files or
00:42:59.960 --> 00:43:04.240
JavaScript files or
00:43:01.760 --> 00:43:07.440
whatever they find the most recently
00:43:04.240 --> 00:43:09.800
accessed 20 files in the same language
00:43:07.440 --> 00:43:12.599
so like if you've opened 20 tabs they
00:43:09.800 --> 00:43:15.559
keep track of which tab you had
00:43:12.599 --> 00:43:18.280
open um and then the actual prompt that
00:43:15.559 --> 00:43:22.119
they send over includes text that is
00:43:18.280 --> 00:43:23.640
before text that's after um similar
00:43:22.119 --> 00:43:26.520
files out of the 20 files that you've
00:43:23.640 --> 00:43:29.480
opened recently um also information from
00:43:26.520 --> 00:43:31.760
imported files and metadata about the
00:43:29.480 --> 00:43:33.079
language and the path so all of this is
00:43:31.760 --> 00:43:37.079
sent to the
00:43:33.079 --> 00:43:38.720
model um and so this is just basically
00:43:37.079 --> 00:43:40.160
it's really good prompt engineering
00:43:38.720 --> 00:43:41.760
right they're figuring out a good way to
00:43:40.160 --> 00:43:44.200
get all of the information that would be
00:43:41.760 --> 00:43:45.680
useful uh for getting this model to work
00:43:44.200 --> 00:43:49.559
into the
00:43:45.680 --> 00:43:50.920
prompt um so I there's much much more
00:43:49.559 --> 00:43:52.839
information in this plug it's a really
00:43:50.920 --> 00:43:57.400
nice blog if you uh if you want to see
00:43:52.839 --> 00:43:57.400
about it but um that's the basic
00:43:57.640 --> 00:44:00.240
any any
00:44:01.240 --> 00:44:07.160
questions okay
00:44:03.520 --> 00:44:11.240
cool yeah is this just what gets sent
00:44:07.160 --> 00:44:13.520
over to theot server or does
00:44:11.240 --> 00:44:15.240
copilot this is what gets sent over to
00:44:13.520 --> 00:44:17.920
the co-pilot server but the way they're
00:44:15.240 --> 00:44:20.960
sending it makes me guess that like all
00:44:17.920 --> 00:44:22.839
of this is red so like they also are
00:44:20.960 --> 00:44:24.559
considering I didn't mention it here but
00:44:22.839 --> 00:44:26.000
they're considering the token limit and
00:44:24.559 --> 00:44:27.599
other stuff like that so that kind of
00:44:26.000 --> 00:44:30.760
makes me feel like this is
00:44:27.599 --> 00:44:30.760
actually the
00:44:32.240 --> 00:44:38.440
pr uh cool
00:44:35.359 --> 00:44:41.040
so another uh thing that you can do is
00:44:38.440 --> 00:44:42.520
retrieval based code generation and
00:44:41.040 --> 00:44:45.640
retrieval based code
00:44:42.520 --> 00:44:47.599
generation uh basically what it does is
00:44:45.640 --> 00:44:50.920
it's like rag for code
00:44:47.599 --> 00:44:53.240
Generation Um and this has been around
00:44:50.920 --> 00:44:55.640
for a while including our work that I
00:44:53.240 --> 00:44:57.680
cited here and a few more in in
00:44:55.640 --> 00:44:59.960
2018 um
00:44:57.680 --> 00:45:03.000
and so one way you can do this is you
00:44:59.960 --> 00:45:07.160
can retrieve similar code from online
00:45:03.000 --> 00:45:09.720
and then use it to basically prompt a
00:45:07.160 --> 00:45:11.920
retrieval augmented language model uh
00:45:09.720 --> 00:45:14.480
this is good if you have a model that's
00:45:11.920 --> 00:45:16.920
not super good at code in the first
00:45:14.480 --> 00:45:19.920
place or you know it's making mistakes
00:45:16.920 --> 00:45:21.680
it's also good if you have a large code
00:45:19.920 --> 00:45:23.040
base like that's inter internal and you
00:45:21.680 --> 00:45:24.200
know the language model was not trained
00:45:23.040 --> 00:45:26.359
on it but you still want to use that
00:45:24.200 --> 00:45:27.559
code base for code generation so it's
00:45:26.359 --> 00:45:29.599
really good if you're working at like a
00:45:27.559 --> 00:45:32.160
big company for example that has a very
00:45:29.599 --> 00:45:33.319
constant coding style but hasn't trained
00:45:32.160 --> 00:45:37.160
its own
00:45:33.319 --> 00:45:39.720
LM um also particularly in code there's
00:45:37.160 --> 00:45:43.559
also documentation uh which can be
00:45:39.720 --> 00:45:46.920
retrieved and so we have new libraries
00:45:43.559 --> 00:45:51.359
all the time right and one frustrating
00:45:46.920 --> 00:45:53.119
thing when using like uh chat jpt or CLA
00:45:51.359 --> 00:45:57.400
or something like that when you're
00:45:53.119 --> 00:45:59.559
writing programs is that it can use old
00:45:57.400 --> 00:46:03.480
versions of libraries that are no longer
00:45:59.559 --> 00:46:05.359
compatible and so um in this paper uh
00:46:03.480 --> 00:46:08.359
which this is one of our papers too we
00:46:05.359 --> 00:46:10.079
called it DOC prompting um basically the
00:46:08.359 --> 00:46:13.720
idea is that
00:46:10.079 --> 00:46:17.440
you have your natural language input and
00:46:13.720 --> 00:46:20.119
then you look up uh similar thing
00:46:17.440 --> 00:46:23.240
similar documentation so you find like
00:46:20.119 --> 00:46:25.319
pigment is a general syntax highlighter
00:46:23.240 --> 00:46:28.160
uh so you can uh find syntax
00:46:25.319 --> 00:46:31.160
highlighting um you can also look up the
00:46:28.160 --> 00:46:32.640
lexer you can look up the HTML formatter
00:46:31.160 --> 00:46:35.119
and then all of the things that have
00:46:32.640 --> 00:46:37.000
similar documentation then you can uh
00:46:35.119 --> 00:46:39.480
append that to the prompt and then have
00:46:37.000 --> 00:46:41.680
that Genera output and we demonstrate
00:46:39.480 --> 00:46:43.200
that this is good both in general but
00:46:41.680 --> 00:46:44.800
also it's particularly good when you're
00:46:43.200 --> 00:46:46.240
dealing with new libraries that haven't
00:46:44.800 --> 00:46:48.280
been seen before or libraries that have
00:46:46.240 --> 00:46:50.119
been updated so this is another thing
00:46:48.280 --> 00:46:53.000
that you can
00:46:50.119 --> 00:46:55.720
do
00:46:53.000 --> 00:46:57.520
cool um another thing that you can do
00:46:55.720 --> 00:47:00.040
with code that you can't do easily with
00:46:57.520 --> 00:47:04.040
natural language is execution
00:47:00.040 --> 00:47:06.119
feedback and so this is a a paper where
00:47:04.040 --> 00:47:09.359
basically they do something that's
00:47:06.119 --> 00:47:10.319
rather simple but they generate multiple
00:47:09.359 --> 00:47:13.359
types of
00:47:10.319 --> 00:47:14.559
code or multiple instances of code so
00:47:13.359 --> 00:47:16.880
they basically sample different
00:47:14.559 --> 00:47:19.960
varieties of code and I was talking
00:47:16.880 --> 00:47:22.720
about like casset K right uh before
00:47:19.960 --> 00:47:25.000
casset K is good if you have some way to
00:47:22.720 --> 00:47:26.520
confirm which output is correct like you
00:47:25.000 --> 00:47:28.040
already have unit tests and you can run
00:47:26.520 --> 00:47:29.440
the unit test and identify which one
00:47:28.040 --> 00:47:31.839
passes the unit test or you can have a
00:47:29.440 --> 00:47:34.160
human check it but in the case when you
00:47:31.839 --> 00:47:35.640
can't do that what can you do and
00:47:34.160 --> 00:47:38.079
basically what you can do is you can
00:47:35.640 --> 00:47:40.800
execute all of the code Snippets that
00:47:38.079 --> 00:47:43.839
the model generated and check if the
00:47:40.800 --> 00:47:48.520
outputs overlap with each other and if
00:47:43.839 --> 00:47:50.680
you have um you know 30 programs that
00:47:48.520 --> 00:47:53.680
all generate very similar outputs then
00:47:50.680 --> 00:47:55.079
those outputs you know then that program
00:47:53.680 --> 00:47:56.520
is probably correct and then you can
00:47:55.079 --> 00:48:00.000
just pick one of them according to some
00:47:56.520 --> 00:48:02.160
criteria Ian specifically in this case
00:48:00.000 --> 00:48:03.960
they picked the program that has the
00:48:02.160 --> 00:48:05.599
lowest base risk like when we talked
00:48:03.960 --> 00:48:09.040
about minimum base risk and the decoding
00:48:05.599 --> 00:48:10.839
much so um they they basically execute a
00:48:09.040 --> 00:48:12.800
lot and then calculate the base risk of
00:48:10.839 --> 00:48:17.000
that
00:48:12.800 --> 00:48:17.000
that cool um
00:48:17.680 --> 00:48:24.440
yeah yeah and so like self consistency
00:48:21.599 --> 00:48:26.079
is a variety of Base risk um and they're
00:48:24.440 --> 00:48:27.640
using base risk here because outputs
00:48:26.079 --> 00:48:30.720
might not be exact the same but being
00:48:27.640 --> 00:48:30.720
closer is probably better
00:48:34.160 --> 00:48:39.040
than
00:48:36.760 --> 00:48:40.559
comp comparison of the code yeah that's
00:48:39.040 --> 00:48:42.880
a good question especially if you use
00:48:40.559 --> 00:48:44.319
something good like uh code BT score to
00:48:42.880 --> 00:48:46.280
do that comparison you might not even
00:48:44.319 --> 00:48:50.280
need to that's
00:48:46.280 --> 00:48:50.280
that I don't think they did that in
00:48:50.559 --> 00:48:57.240
this cool um another interesting thing
00:48:54.920 --> 00:48:59.760
um is there's
00:48:57.240 --> 00:49:04.119
several lines of work on fixing based on
00:48:59.760 --> 00:49:06.720
eror messages so the basic idea is you
00:49:04.119 --> 00:49:08.160
generate code you try to run it you get
00:49:06.720 --> 00:49:13.280
an airor message from it and then you
00:49:08.160 --> 00:49:16.200
feed that back to the llm um in order to
00:49:13.280 --> 00:49:17.520
you know correct the error and like llms
00:49:16.200 --> 00:49:19.119
if you give them an err and you give
00:49:17.520 --> 00:49:20.839
them buggy code they do have some
00:49:19.119 --> 00:49:24.599
capacity to do that especially as you
00:49:20.839 --> 00:49:28.839
get to theer llm so uh this is kind of a
00:49:24.599 --> 00:49:31.200
a nice uh paradigm this paper intercode
00:49:28.839 --> 00:49:33.880
actually generalizes this a bit and it's
00:49:31.200 --> 00:49:38.359
more recent that's why I cited it here
00:49:33.880 --> 00:49:40.000
and uh so this also um like says you can
00:49:38.359 --> 00:49:42.640
do single turn code generation you can
00:49:40.000 --> 00:49:44.960
also say oh could you please try again
00:49:42.640 --> 00:49:46.400
um you can also uh do planning and
00:49:44.960 --> 00:49:48.160
solving and other stuff like that so
00:49:46.400 --> 00:49:49.960
this is a good kind of like environment
00:49:48.160 --> 00:49:52.079
if you're interested in making these
00:49:49.960 --> 00:49:56.720
more like interactive coding assistance
00:49:52.079 --> 00:49:56.720
for example so you could take a look bre
00:49:58.359 --> 00:50:03.359
cool
00:50:00.119 --> 00:50:07.119
um another important topic is code
00:50:03.359 --> 00:50:08.880
synthesis from input output examples so
00:50:07.119 --> 00:50:12.319
actually when you said code generation
00:50:08.880 --> 00:50:14.760
or code synthesis like five years ago or
00:50:12.319 --> 00:50:17.440
10 years ago a lot of people would think
00:50:14.760 --> 00:50:19.440
about this uh so this is actually this
00:50:17.440 --> 00:50:22.440
has been around a lot longer than code
00:50:19.440 --> 00:50:24.160
synthesis um than serious inquiries into
00:50:22.440 --> 00:50:27.680
code synthesis from natural
00:50:24.160 --> 00:50:30.680
language um
00:50:27.680 --> 00:50:33.839
so basically the way this works is it
00:50:30.680 --> 00:50:35.319
can have no natural language whatsoever
00:50:33.839 --> 00:50:39.119
um but you still can try to guess the
00:50:35.319 --> 00:50:42.000
input from uh input output examples when
00:50:39.119 --> 00:50:44.319
would you want to do this so one example
00:50:42.000 --> 00:50:45.839
of this is something called flashfill
00:50:44.319 --> 00:50:48.599
which has been around for a very long
00:50:45.839 --> 00:50:51.839
time in Microsoft Excel and basically
00:50:48.599 --> 00:50:55.400
the way it works is you have one column
00:50:51.839 --> 00:50:58.640
and um the column might be
00:50:55.400 --> 00:50:58.640
like uh
00:50:59.559 --> 00:51:02.880
R new
00:51:03.040 --> 00:51:12.799
big and uh
00:51:06.559 --> 00:51:12.799
else just pick on three because he also
00:51:14.040 --> 00:51:19.599
up and so we have this column and then
00:51:17.160 --> 00:51:19.599
we have like
00:51:20.400 --> 00:51:26.760
gig um and from like one or a couple
00:51:25.160 --> 00:51:28.400
examples basically what it does is it
00:51:26.760 --> 00:51:30.319
tries to induce a program that can
00:51:28.400 --> 00:51:33.319
generate all the other examples properly
00:51:30.319 --> 00:51:35.599
so in this particular case that would be
00:51:33.319 --> 00:51:38.440
um you know like
00:51:35.599 --> 00:51:40.480
split take the first character from the
00:51:38.440 --> 00:51:43.280
first one and all of the last one and
00:51:40.480 --> 00:51:45.280
then concatenate and then M or something
00:51:43.280 --> 00:51:48.280
like that right
00:51:45.280 --> 00:51:50.079
um and so this is useful in some cases
00:51:48.280 --> 00:51:51.599
like you know in Excel when you have
00:51:50.079 --> 00:51:53.359
this long sheet and you want to fill in
00:51:51.599 --> 00:51:56.160
the rest of it and this has actually
00:51:53.359 --> 00:51:57.720
been deployed uh you know in Excel in
00:51:56.160 --> 00:52:00.960
white
00:51:57.720 --> 00:52:02.559
used um if you're interested in this
00:52:00.960 --> 00:52:06.040
topic there's a fair amount of work in
00:52:02.559 --> 00:52:08.839
it um my there's a little bit less work
00:52:06.040 --> 00:52:10.240
now because most people are focusing on
00:52:08.839 --> 00:52:12.400
uh learning programs from natural
00:52:10.240 --> 00:52:14.839
language and other stuff like this but
00:52:12.400 --> 00:52:16.480
uh this slightly older Pap paper called
00:52:14.839 --> 00:52:19.359
interpret explains a bunch of the
00:52:16.480 --> 00:52:22.880
different methods that people used and
00:52:19.359 --> 00:52:25.920
um how you uh like how they compare and
00:52:22.880 --> 00:52:28.119
stuff and also um Joshua ten and bums
00:52:25.920 --> 00:52:29.880
group from MI has done a lot on program
00:52:28.119 --> 00:52:31.319
synthesis from input output examples so
00:52:29.880 --> 00:52:32.359
you could also take a look at that that
00:52:31.319 --> 00:52:35.079
sounds
00:52:32.359 --> 00:52:38.240
interesting um one thing about this is
00:52:35.079 --> 00:52:40.280
these generally are mostly done on
00:52:38.240 --> 00:52:43.319
domain specific languages so they're
00:52:40.280 --> 00:52:46.839
mostly done like only for reg X's or
00:52:43.319 --> 00:52:48.480
they're done only for you know SQL or
00:52:46.839 --> 00:52:50.079
something like that not for the more
00:52:48.480 --> 00:52:51.960
general purpose languages just because
00:52:50.079 --> 00:52:54.079
the problem without any natural language
00:52:51.960 --> 00:52:56.520
specification is harder and so you need
00:52:54.079 --> 00:52:57.520
to like make the search space smaller or
00:52:56.520 --> 00:53:01.559
Additionally you needed to make the
00:52:57.520 --> 00:53:04.440
search small for theable so um that's a
00:53:01.559 --> 00:53:04.440
another thing to know
00:53:04.799 --> 00:53:09.440
about cool um any questions about
00:53:09.480 --> 00:53:14.440
these nice okay so finally in the the
00:53:12.559 --> 00:53:15.599
last few minutes I'd like to talk about
00:53:14.440 --> 00:53:18.480
um code
00:53:15.599 --> 00:53:22.880
LMS and I'm going to go through about
00:53:18.480 --> 00:53:24.599
four of them the first one is codex and
00:53:22.880 --> 00:53:26.200
so yeah actually what I should mention
00:53:24.599 --> 00:53:28.079
is all of the LMS that I talked about up
00:53:26.200 --> 00:53:30.640
until this point are code LMS because
00:53:28.079 --> 00:53:31.680
every LM trains on code so I'm mainly
00:53:30.640 --> 00:53:36.119
going to be talking about one
00:53:31.680 --> 00:53:39.200
specifically for code this time um so
00:53:36.119 --> 00:53:42.480
codex is the first and kind of like
00:53:39.200 --> 00:53:45.880
first really big impact Cod LM um it was
00:53:42.480 --> 00:53:47.720
created by open AI um originally I don't
00:53:45.880 --> 00:53:49.079
know about the deployed model now
00:53:47.720 --> 00:53:51.599
because you know they don't release the
00:53:49.079 --> 00:53:53.799
details of it but originally this was
00:53:51.599 --> 00:53:57.920
trained by continued training from
00:53:53.799 --> 00:53:59.799
gpt3 so they had a text M and then they
00:53:57.920 --> 00:54:03.079
just continued training it on lots and
00:53:59.799 --> 00:54:05.680
lots of code from GitHub um so yeah the
00:54:03.079 --> 00:54:08.799
data was lots of data from GitHub um if
00:54:05.680 --> 00:54:11.280
you did anything on GitHub at any point
00:54:08.799 --> 00:54:14.119
in your life uh you might be uh
00:54:11.280 --> 00:54:17.720
contributing to codep so thank you on
00:54:14.119 --> 00:54:22.440
behalf of open AI a 80 billion dollar
00:54:17.720 --> 00:54:24.599
company and uh importantly it Powers I
00:54:22.440 --> 00:54:27.599
believe it still Powers GitHub
00:54:24.599 --> 00:54:31.160
co-pilot one interesting thing is they
00:54:27.599 --> 00:54:33.119
had a large version of codex um and then
00:54:31.160 --> 00:54:35.799
they had a smaller version of codex
00:54:33.119 --> 00:54:38.359
called code kushman and the thing
00:54:35.799 --> 00:54:40.040
actually powering GitHub co-pilot is not
00:54:38.359 --> 00:54:42.839
the the largest version it's not code Da
00:54:40.040 --> 00:54:46.359
Vinci it's code kushman which is uh
00:54:42.839 --> 00:54:48.680
smaller and much faster and the reason
00:54:46.359 --> 00:54:50.640
why is probably twofold number one um
00:54:48.680 --> 00:54:54.160
you need really fast responses when
00:54:50.640 --> 00:54:55.760
you're you know working on code and
00:54:54.160 --> 00:54:57.440
there's actually in co-pilot there's
00:54:55.760 --> 00:55:00.280
some cach and other stuff like that to
00:54:57.440 --> 00:55:01.960
make your responses very fast as well um
00:55:00.280 --> 00:55:03.400
the second reason is probably it' just
00:55:01.960 --> 00:55:05.040
be too expensive for them to run Da
00:55:03.400 --> 00:55:06.760
Vinci over all the code bases for how
00:55:05.040 --> 00:55:10.400
much they're charging you for co-pilot
00:55:06.760 --> 00:55:12.119
so like every single time you like
00:55:10.400 --> 00:55:14.280
change something in one of your files if
00:55:12.119 --> 00:55:17.079
you're using copilot it's rerunning in
00:55:14.280 --> 00:55:19.359
llm and that would become very expensive
00:55:17.079 --> 00:55:20.599
if you look look at the token count so I
00:55:19.359 --> 00:55:21.839
think they're using a smaller model
00:55:20.599 --> 00:55:22.920
because of that but nonetheless it's
00:55:21.839 --> 00:55:27.039
very
00:55:22.920 --> 00:55:28.640
good um cool
00:55:27.039 --> 00:55:30.680
so now I want to get into some more
00:55:28.640 --> 00:55:33.880
modern models uh the first one I want to
00:55:30.680 --> 00:55:35.520
get into is uh star coder 2 and the
00:55:33.880 --> 00:55:38.359
reason why I want to talk about this
00:55:35.520 --> 00:55:40.160
first is because uh not necessarily that
00:55:38.359 --> 00:55:41.880
it's like absolutely the best one
00:55:40.160 --> 00:55:43.400
although it's very good but it's one of
00:55:41.880 --> 00:55:45.319
the models that actually tells us
00:55:43.400 --> 00:55:47.240
everything about their training data and
00:55:45.319 --> 00:55:50.400
training process and stuff so we know uh
00:55:47.240 --> 00:55:53.039
everything about them so the creator of
00:55:50.400 --> 00:55:54.440
This was um the big science project
00:55:53.039 --> 00:55:56.880
which was led by hugging face and
00:55:54.440 --> 00:55:58.680
service now um
00:55:56.880 --> 00:56:02.079
and includes lots and lots of people
00:55:58.680 --> 00:56:04.960
from various universities and things um
00:56:02.079 --> 00:56:09.319
the architecture is mostly llama style
00:56:04.960 --> 00:56:11.960
it has 3B 7B and 15b variants um one
00:56:09.319 --> 00:56:15.480
interesting thing about all code LMS is
00:56:11.960 --> 00:56:17.680
that they all do long context they all
00:56:15.480 --> 00:56:20.359
do longer context and they all
00:56:17.680 --> 00:56:23.200
reconfigure rope for longer context
00:56:20.359 --> 00:56:25.280
specifically so you know rope has a
00:56:23.200 --> 00:56:28.599
Theta parameter that allows you to tell
00:56:25.280 --> 00:56:31.720
how long the um like sign sine waves and
00:56:28.599 --> 00:56:33.720
stuff like that are and they all always
00:56:31.720 --> 00:56:36.079
um change the parameters so that the
00:56:33.720 --> 00:56:38.599
context is longer so that's another good
00:56:36.079 --> 00:56:38.599
thing to know
00:56:38.640 --> 00:56:44.559
about the the training data section of
00:56:42.000 --> 00:56:48.799
this paper is really fascinating I can
00:56:44.559 --> 00:56:51.240
like it it's a really good way to look
00:56:48.799 --> 00:56:54.160
at you know how much data engineering
00:56:51.240 --> 00:56:55.960
goes into making a good model um and
00:56:54.160 --> 00:56:57.960
just very shortly they give a lot more
00:56:55.960 --> 00:57:00.640
detail in the paper but it's trained on
00:56:57.960 --> 00:57:04.839
code uh including the stack which is
00:57:00.640 --> 00:57:06.920
just a huge uh amount like repository of
00:57:04.839 --> 00:57:08.359
code that I'll talk about in a second
00:57:06.920 --> 00:57:10.559
separately from that it was trained on
00:57:08.359 --> 00:57:13.079
GitHub issues it was trained on poll
00:57:10.559 --> 00:57:16.000
requests Jupiter notebooks keggle
00:57:13.079 --> 00:57:18.319
notebooks documentation and also
00:57:16.000 --> 00:57:23.440
intermediate representations from uh
00:57:18.319 --> 00:57:26.440
llvm so llvm is a uh you know like
00:57:23.440 --> 00:57:28.920
intermediate uh compiler style thing
00:57:26.440 --> 00:57:30.839
that is used for compiling code and it
00:57:28.920 --> 00:57:34.400
was also trained on a few code relevant
00:57:30.839 --> 00:57:38.440
natural language data sets
00:57:34.400 --> 00:57:39.960
um so for pre-processing they do
00:57:38.440 --> 00:57:42.640
something pretty interesting which is
00:57:39.960 --> 00:57:44.240
they add metadata tags such as the repo
00:57:42.640 --> 00:57:48.119
name and the file name and other stuff
00:57:44.240 --> 00:57:49.799
like this uh 50% of the time and they do
00:57:48.119 --> 00:57:51.599
this 50% of the time because they want
00:57:49.799 --> 00:57:54.400
the model to work with them but also be
00:57:51.599 --> 00:57:57.079
robust without them um and so you can
00:57:54.400 --> 00:57:59.839
either add them or not add them at test
00:57:57.079 --> 00:58:03.079
time uh they also do infilling every
00:57:59.839 --> 00:58:05.960
serus code LM does infilling Based
00:58:03.079 --> 00:58:07.480
training um one interesting thing about
00:58:05.960 --> 00:58:08.960
this from the training perspective is
00:58:07.480 --> 00:58:12.000
they actually trained it for four to
00:58:08.960 --> 00:58:14.359
five epochs um which is much more than
00:58:12.000 --> 00:58:17.160
we normally do so normally we only train
00:58:14.359 --> 00:58:18.359
for like one Epoch over you know all of
00:58:17.160 --> 00:58:20.079
the data we have but here they were
00:58:18.359 --> 00:58:21.319
training for monger and that's just
00:58:20.079 --> 00:58:23.359
because the amount of data they can get
00:58:21.319 --> 00:58:24.400
for code is less than the amount of data
00:58:23.359 --> 00:58:27.200
they can get for all the national
00:58:24.400 --> 00:58:30.039
language I
00:58:27.200 --> 00:58:33.200
so the data set that they created is uh
00:58:30.039 --> 00:58:36.119
the stack 2 and this is a code
00:58:33.200 --> 00:58:37.839
pre-training data set um one interesting
00:58:36.119 --> 00:58:40.039
thing that they thought about was uh
00:58:37.839 --> 00:58:42.960
license considerations so I talked about
00:58:40.039 --> 00:58:44.480
the um how copyright is a problem when
00:58:42.960 --> 00:58:46.640
trading large language models two
00:58:44.480 --> 00:58:48.880
classes ago and so here they
00:58:46.640 --> 00:58:50.119
specifically tried to find things with
00:58:48.880 --> 00:58:52.520
permissive
00:58:50.119 --> 00:58:53.880
licenses and so what they did is they
00:58:52.520 --> 00:58:57.000
basically looked at the license on
00:58:53.880 --> 00:58:59.520
GitHub um and if the GitHub license was
00:58:57.000 --> 00:59:01.440
permissive they marked it as permissive
00:58:59.520 --> 00:59:02.880
um then they tried to detect licenses
00:59:01.440 --> 00:59:05.720
and then um if all of them were
00:59:02.880 --> 00:59:08.000
permissive they marked it as
00:59:05.720 --> 00:59:10.480
permissive this is a huge table that
00:59:08.000 --> 00:59:14.160
they have in the paper of all of the
00:59:10.480 --> 00:59:15.480
data that they have and um you know I'm
00:59:14.160 --> 00:59:16.920
not going to go through all of this
00:59:15.480 --> 00:59:18.920
obviously but what you can see is some
00:59:16.920 --> 00:59:22.480
of the biggest data sets are like
00:59:18.920 --> 00:59:26.280
Java um
00:59:22.480 --> 00:59:28.640
PHP markdown
00:59:26.280 --> 00:59:30.039
and uh Python and other stuff like that
00:59:28.640 --> 00:59:32.240
so you can see the major programming
00:59:30.039 --> 00:59:35.559
languages have lots of data but there's
00:59:32.240 --> 00:59:38.400
also a long tail so if you like your uh
00:59:35.559 --> 00:59:40.440
you know more esoteric uh but cool
00:59:38.400 --> 00:59:43.960
programming languages like rust yes it
00:59:40.440 --> 00:59:46.160
has rust too so um we can do all all of
00:59:43.960 --> 00:59:46.160
those
00:59:46.480 --> 00:59:53.079
things so the next model that I'd like
00:59:49.799 --> 00:59:55.200
to talk about is cod llama and cod llama
00:59:53.079 --> 00:59:57.920
is another competitive model it came out
00:59:55.200 --> 00:59:59.480
a little bit before star coder and star
00:59:57.920 --> 01:00:02.680
coder 2 and deep sea coder which I'm
00:59:59.480 --> 01:00:04.079
going to talk about um this is a created
01:00:02.680 --> 01:00:08.319
by
01:00:04.079 --> 01:00:11.160
meta and um the architecture is the same
01:00:08.319 --> 01:00:14.280
as llama 2 uh basically and they did
01:00:11.160 --> 01:00:16.400
continued training from llama 2 um but
01:00:14.280 --> 01:00:18.000
they trained it on longer input contexts
01:00:16.400 --> 01:00:21.720
and they also extended the length of
01:00:18.000 --> 01:00:23.559
rope so uh those are you know standard
01:00:21.720 --> 01:00:26.680
things for code language
01:00:23.559 --> 01:00:28.680
models it was trained on DED code and
01:00:26.680 --> 01:00:30.400
also synthetically created instruction
01:00:28.680 --> 01:00:33.280
data so they created like instruction
01:00:30.400 --> 01:00:37.920
tuning data specifically for
01:00:33.280 --> 01:00:39.480
code um and the training was incremental
01:00:37.920 --> 01:00:42.559
with various data sets and what I mean
01:00:39.480 --> 01:00:45.599
by this is they trained on 500 billion
01:00:42.559 --> 01:00:47.599
uh I believe tokens of code and then
01:00:45.599 --> 01:00:50.400
they did long context fine tuning on 20
01:00:47.599 --> 01:00:52.599
billion tokens and then they also did
01:00:50.400 --> 01:00:55.400
instruction tuning they also have a
01:00:52.599 --> 01:00:57.079
python specific one and the reason why
01:00:55.400 --> 01:00:59.640
they have a p specific one is not
01:00:57.079 --> 01:01:02.319
because python is more import important
01:00:59.640 --> 01:01:03.839
uh uh necessarily but because a lot of
01:01:02.319 --> 01:01:05.559
the benchmarks are in Python because
01:01:03.839 --> 01:01:06.920
machine learning people like who are
01:01:05.559 --> 01:01:09.240
creating benchmarks they also like
01:01:06.920 --> 01:01:11.200
python so python is more common in the
01:01:09.240 --> 01:01:14.240
benchmarks so they basically wanted to
01:01:11.200 --> 01:01:15.720
do well on the benchmarks I think uh and
01:01:14.240 --> 01:01:17.920
and created a data set that does well in
01:01:15.720 --> 01:01:19.240
the benchmarks but um if you are
01:01:17.920 --> 01:01:23.160
creating python you can use the code
01:01:19.240 --> 01:01:25.280
llama python it's better at pipelines so
01:01:23.160 --> 01:01:28.000
um and then the final one I'd like to
01:01:25.280 --> 01:01:29.839
talk about is is a deep seek coder uh
01:01:28.000 --> 01:01:32.079
this is notable because it's a very
01:01:29.839 --> 01:01:34.599
strong model it it's maybe the strongest
01:01:32.079 --> 01:01:38.799
model on average over all the code
01:01:34.599 --> 01:01:41.599
models um they did 87% the data is not
01:01:38.799 --> 01:01:44.640
super clear but they did 87% source code
01:01:41.599 --> 01:01:46.359
10% English um from markdown in stock
01:01:44.640 --> 01:01:51.160
exchange and 3% Chinese because it's
01:01:46.359 --> 01:01:53.559
from a Chinese company deep seek um and
01:01:51.160 --> 01:01:54.960
they did standard prepr uh but one
01:01:53.559 --> 01:01:57.319
interesting thing they did is they
01:01:54.960 --> 01:01:59.200
included Library dependencies so they
01:01:57.319 --> 01:02:01.799
basically crawled the dependency graph
01:01:59.200 --> 01:02:03.640
of libraries pulled out files from the
01:02:01.799 --> 01:02:06.000
libraries that were referenced and then
01:02:03.640 --> 01:02:07.440
used them in training and so that's
01:02:06.000 --> 01:02:09.319
particularly useful if you want the
01:02:07.440 --> 01:02:12.920
model to be able to reference external
01:02:09.319 --> 01:02:14.039
libraries well um so that's kind of an
01:02:12.920 --> 01:02:17.279
interesting
01:02:14.039 --> 01:02:19.599
thing um the architecture is pretty
01:02:17.279 --> 01:02:22.960
standard it's llama likee with 1.3
01:02:19.599 --> 01:02:24.599
billion 6.7 billion in 33b variants and
01:02:22.960 --> 01:02:27.279
it has a reconfigured work like the
01:02:24.599 --> 01:02:30.520
others and they on two trillion
01:02:27.279 --> 01:02:34.200
tokens um so then a question becomes
01:02:30.520 --> 01:02:36.680
which one to use um and I created a
01:02:34.200 --> 01:02:39.160
summary here um all of them have
01:02:36.680 --> 01:02:40.760
somewhat similar performance uh this is
01:02:39.160 --> 01:02:42.760
they're compared in the star coder 2
01:02:40.760 --> 01:02:45.640
paper so you can go in and look at
01:02:42.760 --> 01:02:48.160
details at the starcode to paper um
01:02:45.640 --> 01:02:51.119
deeps coder seems to be strong on
01:02:48.160 --> 01:02:52.799
standard programming tasks um whereas
01:02:51.119 --> 01:02:54.799
star coder seems to be strong on data
01:02:52.799 --> 01:02:56.680
science notebooks so like on average
01:02:54.799 --> 01:02:59.160
it's better at kind of sound notebooks
01:02:56.680 --> 01:03:02.079
but all of them are good models um all
01:02:59.160 --> 01:03:05.440
of them are not quite as good as uh like
01:03:02.079 --> 01:03:08.920
gp4 quad on like they're very uh you
01:03:05.440 --> 01:03:10.799
know more complex tasks but uh they're
01:03:08.920 --> 01:03:12.359
available and you can find to them and
01:03:10.799 --> 01:03:16.880
do other things like that as
01:03:12.359 --> 01:03:21.599
well one caveat about the Deep seek
01:03:16.880 --> 01:03:24.640
thing is actually if I go back to this
01:03:21.599 --> 01:03:27.559
slide um a lot of the models up here are
01:03:24.640 --> 01:03:29.640
deep seek um so you do need to be a
01:03:27.559 --> 01:03:31.400
little bit careful about like
01:03:29.640 --> 01:03:34.400
interpreting their human Evel results
01:03:31.400 --> 01:03:36.319
because it's possible that the model uh
01:03:34.400 --> 01:03:38.799
was trained on data very similar to
01:03:36.319 --> 01:03:40.279
human eval or something like that so do
01:03:38.799 --> 01:03:42.880
take that with a grain of salt but even
01:03:40.279 --> 01:03:44.520
on other data sets where presumably the
01:03:42.880 --> 01:03:46.760
model has not seen those data sets it
01:03:44.520 --> 01:03:49.920
still does very well so it's not like
01:03:46.760 --> 01:03:51.480
it's um you know as you can see it's
01:03:49.920 --> 01:03:54.640
still one of the most competitive code
01:03:51.480 --> 01:03:57.680
models even on this new LCB um data set
01:03:54.640 --> 01:04:01.359
so uh that's want into the
01:03:57.680 --> 01:04:03.000
a cool um that's all I have for today I
01:04:01.359 --> 01:04:04.359
you know I love to talk about this topic
01:04:03.000 --> 01:04:06.480
I've done a lot of research on it so I'm
01:04:04.359 --> 01:04:11.200
happy to discuss any questions if people
01:04:06.480 --> 01:04:14.720
have them either in front of everyone or
01:04:11.200 --> 01:04:14.720
after any any
01:04:16.480 --> 01:04:24.160
questions uh yeah just wondering there a
01:04:20.359 --> 01:04:27.720
like enfor the outut during using things
01:04:24.160 --> 01:04:27.720
other than models
01:04:30.599 --> 01:04:36.599
yeah great question is there a way to
01:04:33.640 --> 01:04:38.200
enforce uh restrictions at decoding time
01:04:36.599 --> 01:04:39.760
other than using the model's uh
01:04:38.200 --> 01:04:42.240
probabilities because this is code and
01:04:39.760 --> 01:04:42.240
we know the
01:04:42.440 --> 01:04:51.079
syntax yes and no um there
01:04:46.319 --> 01:04:53.200
are for code it's not always immediately
01:04:51.079 --> 01:04:54.400
obvious like I mean one one thing you
01:04:53.200 --> 01:04:55.960
could do is just generate a bunch of
01:04:54.400 --> 01:04:58.520
results and throw out all the syntax
01:04:55.960 --> 01:04:59.480
incorrect on that's easy right um but if
01:04:58.520 --> 01:05:02.520
you don't want to do that and you want
01:04:59.480 --> 01:05:04.839
to do it at decoding time it's dependent
01:05:02.520 --> 01:05:07.480
on you being able to have an incremental
01:05:04.839 --> 01:05:09.079
syntax parser that allows you to like
01:05:07.480 --> 01:05:12.400
throw out bad
01:05:09.079 --> 01:05:14.160
hypotheses like incrementally and that's
01:05:12.400 --> 01:05:16.240
possible that's very easy for some
01:05:14.160 --> 01:05:17.200
languages and not possible not as easy
01:05:16.240 --> 01:05:20.559
for other
01:05:17.200 --> 01:05:23.720
languages um one really big thing right
01:05:20.559 --> 01:05:26.599
now is Json so like a lot of the time
01:05:23.720 --> 01:05:28.319
people want to Output Json uh in you
01:05:26.599 --> 01:05:31.559
know then par the Json and use it in
01:05:28.319 --> 01:05:36.640
some Downstream test and there actually
01:05:31.559 --> 01:05:36.640
are libraries um just to give a
01:05:38.559 --> 01:05:45.839
few um here's one this Library called
01:05:42.640 --> 01:05:48.799
outlines um is one that basically allows
01:05:45.839 --> 01:05:50.440
you to incorporate syntactic constraints
01:05:48.799 --> 01:05:53.240
through like weighted finite State
01:05:50.440 --> 01:05:55.160
automata and other stuff like this um to
01:05:53.240 --> 01:05:57.680
allow you to throw away anything that
01:05:55.160 --> 01:06:02.039
doesn't here to your grammar another
01:05:57.680 --> 01:06:02.039
popular one which
01:06:02.720 --> 01:06:06.880
is nice but a little bit more
01:06:07.160 --> 01:06:12.760
complicated is
01:06:09.799 --> 01:06:15.160
um this one uh
01:06:12.760 --> 01:06:17.200
guidance so if you want to look at like
01:06:15.160 --> 01:06:19.720
constrained generation of outputs I
01:06:17.200 --> 01:06:21.640
would definitely recommend uh looking at
01:06:19.720 --> 01:06:22.839
one of these two either outlines or or
01:06:21.640 --> 01:06:24.440
guidance and they both give you
01:06:22.839 --> 01:06:26.520
different ways to add constraints to
01:06:24.440 --> 01:06:29.079
Output um we did actually talk about
01:06:26.520 --> 01:06:31.200
outlines a little bit during the like uh
01:06:29.079 --> 01:06:34.599
generation class but um we didn't go
01:06:31.200 --> 01:06:35.760
into a lot of details so uh yeah but I I
01:06:34.599 --> 01:06:39.559
would recommend
01:06:35.760 --> 01:06:39.559
this cool any other
01:06:39.599 --> 01:06:43.920
questions okay if not uh I guess we can
01:06:42.079 --> 01:06:47.880
finish up and I'm happy to talk we have
01:06:43.920 --> 01:06:47.880
a little bit of extra time
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