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category stringclasses 8 values	messages listlengths 2 2	prompt stringlengths 5 23.6k
classification	[ { "content": "Are the these examples of deserts or desserts? Cake, pie, cookies, and ice cream.", "role": "user" }, { "content": "These four examples are desserts not deserts.", "role": "assistant" } ]	Are the these examples of deserts or desserts? Cake, pie, cookies, and ice cream.
information_extraction	[ { "content": "A geoboard is a mathematical manipulative used to explore basic concepts in plane geometry such as perimeter, area and the characteristics of triangles and other polygons. It consists of a physical board with a certain number of nails half driven in, around which are wrapped geo bands that are made of rubber. Normal rubber bands can also be used. Geoboards were invented and popularized in the 1950s by Egyptian mathematician Caleb Gattegno (1911-1988). What is a geoboard made of?", "role": "user" }, { "content": "A geoboard is made of a physical board, some nails, and geo bands that could be rubber bands.", "role": "assistant" } ]	A geoboard is a mathematical manipulative used to explore basic concepts in plane geometry such as perimeter, area and the characteristics of triangles and other polygons. It consists of a physical board with a certain number of nails half driven in, around which are wrapped geo bands that are made of rubber. Normal rubber bands can also be used. Geoboards were invented and popularized in the 1950s by Egyptian mathematician Caleb Gattegno (1911-1988). What is a geoboard made of?
brainstorming	[ { "content": "Is Star Wars Science Fiction or Fantasy?", "role": "user" }, { "content": "While Star Wars is considered science fiction by the general public, most sci-fi and fantasy fans would classify Star Wars in the fantasy genre. The themes of the story rely on mythology and the hero's journey as opposed to exploration of scientific and philosophical concepts.", "role": "assistant" } ]	Is Star Wars Science Fiction or Fantasy?
general_qa	[ { "content": "If McDonald’s were to go out of business and disappear, would the world be a better or worse place?", "role": "user" }, { "content": "McDonald’s has more than 36 thousand locations in 190 countries, and people still cannot believe that McDonald's would soon put a period to the honorable accomplishment they did in the last several years making 4.73 billion dollars in a year as a whole. I do believe McDonald’s disappearance would have some difference in some communities in the short term since there are 210,000 people working at McDonald’s. And also, it can have environmental, and medical benefits that can make the world a better place.\n The world is hugely affected by McDonald’s action, which makes the disappearance of McDonald’s could make the world a better place although it could have some negative influence on equality and education. Although McDonald's helps organizations that help people who lack education, suffer in poverty, or fight against discrimination, the positive impact of McDonald’s departure makes the world a better place. For example, since McDonald’s cut down the forest to build a cow farm, it can cause deforestation that is threatening the ecosystem and animal habitats. In addition, McDonald’s produces 2 million tons of packaging each year. This means that the retirement of McDonald’s could prevent habitat loss, deforestation, and even waste problems and have a huge, positive impact on the environment.\n However, the change in McDonald's retirement causes would vary between different stakeholders, but overall, it would only have a mild, or little difference between people’s lives. Although McDonald’s disappearance might shock many people, after a short term, the other fast-food restaurants would replace McDonald’s, so fast-food lovers could adapt to the new environment. Furthermore, although 210,000 people could lose their job, it is still possible for them to change to another job, or have an opportunity to change into another job that they have an interest in. Therefore, McDonald’s disappearance could have little or no difference at all to some people, and withdraw from all the honorable accomplishments.\n In conclusion, I think that the disappearance of McDonald’s can make the world a better place for environmental, and medical reasons although it could have negative effects in some communities, and that McDonald’s would not be forgotten by the joy and honorable achievements that they have achieved in the last few years.", "role": "assistant" } ]	If McDonald’s were to go out of business and disappear, would the world be a better or worse place?
open_qa	[ { "content": "What awards does someone need to be an EGOT winner?", "role": "user" }, { "content": "The person would need an Emmy, a Grammy, an Oscar, and a Tony award to get EGOT status.", "role": "assistant" } ]	What awards does someone need to be an EGOT winner?
brainstorming	[ { "content": "Give me a list of 5 key challenges faced by users moving from a Windows laptop to a Macbook", "role": "user" }, { "content": "1. Incompatibility between MS office documents and MacOS native applications like pages and numbers\n2. Inability to copy content on and off of the laptop easily\n3. Learning the differences between the windows keyboard and the macbook keyboard commands to do simple tasks - like right click or taking a screenshot\n4. The change in process to deploy software. \n5. Navigating the difference between the layout and structure of WIndows explorer vs Macbook finder", "role": "assistant" } ]	Give me a list of 5 key challenges faced by users moving from a Windows laptop to a Macbook
open_qa	[ { "content": "Who are the most famous cricket players of all time?", "role": "user" }, { "content": "Sachin Tendulkar, Don Bradman, Shane Warne, Brian Lara, Viv Richards, Imran Khan, Ian Botham and Ricky Ponting are amongst the most famous cricket players of all time.", "role": "assistant" } ]	Who are the most famous cricket players of all time?
open_qa	[ { "content": "When was the Indian National Flag adopted", "role": "user" }, { "content": "22nd July 1947", "role": "assistant" } ]	When was the Indian National Flag adopted
closed_qa	[ { "content": "Charles Inglis was the second son of Dr. Alexander Inglis (a general practitioner in Worcester) and his first wife, Florence, the daughter of newspaper proprietor John Frederick Feeney. His elder brother was the historian John Alexander Inglis FRSE Their father, Alexander Inglis was born in Scotland to a respectable family – his grandfather, John Inglis, was an Admiral in the Royal Navy and had captained HMS Belliqueux at the Battle of Camperdown in 1797.\n\nCharles Inglis was born on 31 July 1875. He was not expected to survive and was hurriedly baptised in his father's drawing room; his mother died from complications eleven days later. His family moved to Cheltenham and Inglis was schooled at Cheltenham College from 1889 to 1894. In his final year, he was elected head boy and received a scholarship to study the Mathematics Tripos at King's College, Cambridge. Inglis was 22nd wrangler[nb 1] when he received his Bachelor of Arts degree in 1897; he remained for a fourth year, achieving first class honours in Mechanical Sciences. Inglis was a keen sportsman and enjoyed long-distance running, walking, mountaineering and sailing. At Cambridge, he nearly achieved a blue for long-distance running but was forced to withdraw from a significant race because of a pulled muscle. He was also a follower of the Cambridge University Rugby Union team, watching their matches at Grange Road.\n\nAfter graduation, Inglis began work as an apprentice for the civil engineering firm of John Wolfe-Barry & Partners. He worked as a draughtsman in the drawing office for several months before being placed with Alexander Gibb, who was acting as resident engineer on an extension to the Metropolitan District Railway between Whitechapel and Bow. Inglis was responsible for the design and supervision of all thirteen bridges on the route. It was during this time that he began his lifelong study of vibration and its effects on materials, particularly bridges. Given this paragraph about Charles Inglis, give answers to the following: his father's name, his colleague's name, and his grandfather's occupation.", "role": "user" }, { "content": "Alexander Inglis, Alexander Gibb, Admiral in the Royal Navy", "role": "assistant" } ]	Charles Inglis was the second son of Dr. Alexander Inglis (a general practitioner in Worcester) and his first wife, Florence, the daughter of newspaper proprietor John Frederick Feeney. His elder brother was the historian John Alexander Inglis FRSE Their father, Alexander Inglis was born in Scotland to a respectable family – his grandfather, John Inglis, was an Admiral in the Royal Navy and had captained HMS Belliqueux at the Battle of Camperdown in 1797. Charles Inglis was born on 31 July 1875. He was not expected to survive and was hurriedly baptised in his father's drawing room; his mother died from complications eleven days later. His family moved to Cheltenham and Inglis was schooled at Cheltenham College from 1889 to 1894. In his final year, he was elected head boy and received a scholarship to study the Mathematics Tripos at King's College, Cambridge. Inglis was 22nd wrangler[nb 1] when he received his Bachelor of Arts degree in 1897; he remained for a fourth year, achieving first class honours in Mechanical Sciences. Inglis was a keen sportsman and enjoyed long-distance running, walking, mountaineering and sailing. At Cambridge, he nearly achieved a blue for long-distance running but was forced to withdraw from a significant race because of a pulled muscle. He was also a follower of the Cambridge University Rugby Union team, watching their matches at Grange Road. After graduation, Inglis began work as an apprentice for the civil engineering firm of John Wolfe-Barry & Partners. He worked as a draughtsman in the drawing office for several months before being placed with Alexander Gibb, who was acting as resident engineer on an extension to the Metropolitan District Railway between Whitechapel and Bow. Inglis was responsible for the design and supervision of all thirteen bridges on the route. It was during this time that he began his lifelong study of vibration and its effects on materials, particularly bridges. Given this paragraph about Charles Inglis, give answers to the following: his father's name, his colleague's name, and his grandfather's occupation.
closed_qa	[ { "content": "U.S. News & World Report ranks UVA tied for 25th among national universities overall, 3rd among public universities and tied for 110th among global universities in its 2022 report, 8th best business program and 3rd best business management program. According to this paragraph, what is UVA's overall ranking among national universities?", "role": "user" }, { "content": "UVA tied for 25th among national universities.", "role": "assistant" } ]	U.S. News & World Report ranks UVA tied for 25th among national universities overall, 3rd among public universities and tied for 110th among global universities in its 2022 report, 8th best business program and 3rd best business management program. According to this paragraph, what is UVA's overall ranking among national universities?
open_qa	[ { "content": "Who gave the slogan of Quit India and Simon Go Back?", "role": "user" }, { "content": "Yusuf Meherally", "role": "assistant" } ]	Who gave the slogan of Quit India and Simon Go Back?
open_qa	[ { "content": "Which artist made the hit song One More Time?", "role": "user" }, { "content": "Daft Punk", "role": "assistant" } ]	Which artist made the hit song One More Time?
general_qa	[ { "content": "How do I cook an egg over easy?", "role": "user" }, { "content": "To cook an egg over easy you will need a pan, stove, oil (or butter), an egg, and a spatula. Begin by placing the pan on the stove with a minimal amount of oil or butter. Heat the pan on the stove until it is hot. What’s the pan is hot crack the egg in the middle of the pan and let it cook until the egg white becomes solid. At which point flip the egg and let it cook until the desired consistency of the yoke. Finally remove from the pan and enjoy!", "role": "assistant" } ]	How do I cook an egg over easy?
information_extraction	[ { "content": "From the time of the character's debut until 1988, Yogi was voiced by voice actor Daws Butler. Butler died in 1988; his last performance as Yogi was in the television film Yogi and the Invasion of the Space Bears.\n\nIn 1983, a Yogi Bear balloon made its first appearance in the Macy's Thanksgiving Day Parade, becoming the final balloon that year. That same year, he appeared on a float named A Hanna-Barbera Christmas alongside many other Hanna-Barbera characters, as they cleaned up the streets of Broadway. The performance was bookended with animated segments featuring Yogi and Boo-Boo, voiced by Mel Blanc and Butler, respectively.\n\nAfter Butler's death in 1988, Greg Burson stepped in to perform the role; Butler had taught Burson personally how to voice Yogi as well as his other characters. Worsening alcoholism and a legal incident led to Burson's firing in 2004 and eventually his death in 2008.\n\nYogi's current voice actor is Jeff Bergman. Bergman and Billy West also performed the character throughout the 1990s and early to mid-2000s for various Cartoon Network and Boomerang commercials and bumpers. List the people who voiced Yogi Bear in the 1980s.", "role": "user" }, { "content": "Daws Butler voiced Yogi Bear until 1988. Greg Burson took over for the rest of the 1980s.", "role": "assistant" } ]	From the time of the character's debut until 1988, Yogi was voiced by voice actor Daws Butler. Butler died in 1988; his last performance as Yogi was in the television film Yogi and the Invasion of the Space Bears. In 1983, a Yogi Bear balloon made its first appearance in the Macy's Thanksgiving Day Parade, becoming the final balloon that year. That same year, he appeared on a float named A Hanna-Barbera Christmas alongside many other Hanna-Barbera characters, as they cleaned up the streets of Broadway. The performance was bookended with animated segments featuring Yogi and Boo-Boo, voiced by Mel Blanc and Butler, respectively. After Butler's death in 1988, Greg Burson stepped in to perform the role; Butler had taught Burson personally how to voice Yogi as well as his other characters. Worsening alcoholism and a legal incident led to Burson's firing in 2004 and eventually his death in 2008. Yogi's current voice actor is Jeff Bergman. Bergman and Billy West also performed the character throughout the 1990s and early to mid-2000s for various Cartoon Network and Boomerang commercials and bumpers. List the people who voiced Yogi Bear in the 1980s.
open_qa	[ { "content": "Which TV show features a team of teenagers that power up with powers given to them by Zordon to save the world from monsters?", "role": "user" }, { "content": "The Mighty Morphin Power Rangers.", "role": "assistant" } ]	Which TV show features a team of teenagers that power up with powers given to them by Zordon to save the world from monsters?
brainstorming	[ { "content": "Why Netherland is famous?", "role": "user" }, { "content": "Because it's known for a nice country", "role": "assistant" } ]	Why Netherland is famous?
summarization	[ { "content": "Shawboro is an unincorporated community located in Currituck County, North Carolina, United States.\n\nThe area takes its name from Henry Marchmore Shaw.\n\nThe post office is located close to the intersection of Shawboro Road (NC 34) and North Indian Town Road (SR 1147). The ZIP Code is 27973. The Shawboro area is covered by three different telephone exchanges: Elizabeth City, Moyock, and Shiloh.\n\nShawboro is served by Crawford Township fire and rescue services as well as Shawboro Elementary School, a K–5 school with approximately 500 students.\n\nCulong, the Shaw House, and Twin Houses are listed on the National Register of Historic Places. Where is the post office of Shawboro located?", "role": "user" }, { "content": "The post office of Shawboro, North Carolina is located close to the intersection of NC 34 Shawboro road and North Indian Town Road (SR 1147).", "role": "assistant" } ]	Shawboro is an unincorporated community located in Currituck County, North Carolina, United States. The area takes its name from Henry Marchmore Shaw. The post office is located close to the intersection of Shawboro Road (NC 34) and North Indian Town Road (SR 1147). The ZIP Code is 27973. The Shawboro area is covered by three different telephone exchanges: Elizabeth City, Moyock, and Shiloh. Shawboro is served by Crawford Township fire and rescue services as well as Shawboro Elementary School, a K–5 school with approximately 500 students. Culong, the Shaw House, and Twin Houses are listed on the National Register of Historic Places. Where is the post office of Shawboro located?
general_qa	[ { "content": "What is the difference between Kinesis and Kafka?", "role": "user" }, { "content": "Kinesis and Kafka are both distributed streaming platforms that allow you to ingest, process, and store large volumes of streaming data. The main difference between the two is that Kinesis Data Streams is a fully managed service provided by AWS, while Kafka is an open source project that can be run on any infrastructure.", "role": "assistant" } ]	What is the difference between Kinesis and Kafka?
summarization	[ { "content": "Frost became interested in sport at a young age and was an all-round athlete. He won medals for running and swimming and was a prominent member of the Winchester Harriers sports club. He particularly excelled as a cyclist, entering competitive events at an early age and holding several amateur cycling records. In 1898, Frost won, within the space of a fortnight, Britain's two leading cycle racing trophies, the Bath Road 100 and the Cawardine Challenge Cup, as well as the national championship. By the time he retired he had won every important amateur cycling trophy in the nation.\n\nHe is particularly remembered for winning the Carwardine Challenge Cup, a 100-mile race held annually at the Herne Hill race track. This huge cup was donated by the vice-president of the Anerley Bicycle Club in 1895 and was made from 650 ounces of silver, with ornamentation of solid gold. The tradition at the time was that if someone won a trophy three times they could keep it. Frost, riding for the Bath Road club in Hayes, first won it in 1896, repeated the victory in 1898 and was victorious for the third time in 1900. The cup is occasionally exhibited in Winchester. Which trophy was Frost able to keep and why?", "role": "user" }, { "content": "Frost was a prominent cyclist. In the late 1800s and early 1900s when he was active he was able to win the Carwardine Cup trophy and keep it. The tradition at the time was that if one could win a trophy 3 times, one could keep the trophy. Frost won this trophy in 1896, 1898 and 1900.", "role": "assistant" } ]	Frost became interested in sport at a young age and was an all-round athlete. He won medals for running and swimming and was a prominent member of the Winchester Harriers sports club. He particularly excelled as a cyclist, entering competitive events at an early age and holding several amateur cycling records. In 1898, Frost won, within the space of a fortnight, Britain's two leading cycle racing trophies, the Bath Road 100 and the Cawardine Challenge Cup, as well as the national championship. By the time he retired he had won every important amateur cycling trophy in the nation. He is particularly remembered for winning the Carwardine Challenge Cup, a 100-mile race held annually at the Herne Hill race track. This huge cup was donated by the vice-president of the Anerley Bicycle Club in 1895 and was made from 650 ounces of silver, with ornamentation of solid gold. The tradition at the time was that if someone won a trophy three times they could keep it. Frost, riding for the Bath Road club in Hayes, first won it in 1896, repeated the victory in 1898 and was victorious for the third time in 1900. The cup is occasionally exhibited in Winchester. Which trophy was Frost able to keep and why?
information_extraction	[ { "content": "The twelve-member basketball selection committee is made up of athletic directors and conference commissioners throughout Division I men's and women's athletics with separate committees for the men's and women's tournaments. The committees consist of one member selected from each of the five autonomy conferences and three members selected from the seven highest-ranked nonautonomy conferences based on basketball success. The remaining four members are selected from the 20 other conferences. All appointments are for five years. Historically the men's selection committee consisted of all men, and the women's selection committee consisted of all women. However, recently women have been serving on the men’s committee (including Judy Rose, Lynn Hickey, Janet Cone, and current member Bernadette McGlade), and men have been serving on the women’s committee (including Richard Ensor and current member Jeff Konya). The tournament selection is only part of the committee members' duties; the panels meet year-round (in-person or through conference calls) to discuss the tournament and its administration, evaluate teams, assign tournament game officials, and determine future tournament sites. Extract all of the names of people mentioned in this paragraph and list them using bullets in the format {Name}", "role": "user" }, { "content": "• Judy Rose\n• Lynn Hickey\n• Janet Cone\n• Bernadette McGlade\n• Richard Ensor\n• Jeff Konya", "role": "assistant" } ]	The twelve-member basketball selection committee is made up of athletic directors and conference commissioners throughout Division I men's and women's athletics with separate committees for the men's and women's tournaments. The committees consist of one member selected from each of the five autonomy conferences and three members selected from the seven highest-ranked nonautonomy conferences based on basketball success. The remaining four members are selected from the 20 other conferences. All appointments are for five years. Historically the men's selection committee consisted of all men, and the women's selection committee consisted of all women. However, recently women have been serving on the men’s committee (including Judy Rose, Lynn Hickey, Janet Cone, and current member Bernadette McGlade), and men have been serving on the women’s committee (including Richard Ensor and current member Jeff Konya). The tournament selection is only part of the committee members' duties; the panels meet year-round (in-person or through conference calls) to discuss the tournament and its administration, evaluate teams, assign tournament game officials, and determine future tournament sites. Extract all of the names of people mentioned in this paragraph and list them using bullets in the format {Name}
summarization	[ { "content": "Thysanozoon nigropapillosum is a species of polyclad flatworms belonging to the family Pseudocerotidae. Some common names include gold-speckled flatworm, marine flatworm, yellow papillae flatworm, yellow-spotted flatworm, and yellow-spotted polyclad flatworm.Thysanozoon nigropapillosum has a long body and broad shape. They grow up to 3 in (76 mm). The dorsal surface is deep black and covered with numerous yellow-tipped papillae varying in size. The ventral surface is dark brown. The outer margin of the body is slightly wavy and bordered in opaque white. They have small, ear-like pseudotentacles in the middle of the anterior end. They swim by propelling themselves through the water with a rhythmic undulating motion of the body.This species is widespread in the tropical Indo-Pacific.Thysanozoon nigropapillosum is quite common along the external reef in the shallow sub-tidal zone. It can swim by undulating and rhythmically contracting the body margins. It feeds on tunicates, using its mouth and large pharynx to engulf Didemnum spp., and later regurgitates food pellets containing the calcareous spicules present in their tunics. Polyclad flatworms are hermaphrodites, with each one of a pair of flatworms trying to inseminate the other. Transfer of sperm may be by hypodermic insemination, but this is made difficult in this species by the presence of the papillae, and dermal impregnation often occurs. This involves sperm entering the body directly after being placed in the marginal region of the skin. Such dermal insemination is facilitated by the fact that the flatworm has two penises which are strengthened by the presence of rhabdites, and these can grip the margin of the partner and keep hold, even while the recipient is swimming.World Database of Marine SpeciesSeaslugsWormsZipcodezooGwannonPhotos of Thysanozoon nigropapillosum on Sealife Collection Please summarize what Thysanozoon nigropapillosum are", "role": "user" }, { "content": "Thysanozoon nigropapillosum is a species of polyclad flatworms belonging to the family Pseudocerotidae. Thysanozoon nigropapillosum are commonly named as gold-speckled flatorm, marine flatworm, yellow paipllae flatworm, yellow spotted flatworm, and yellow spotty polyclad. They have a long body and broad shape, and can grow up to 3 inches (76mm). They are widespread in the Indo-Pacific, and feed on tunicates, and later regurgitates food pellets that contain calcareous spicules that are present in their tunics. The Polyclad flatworms are hermaphrodites, with each pair of flatworms attempt to inseminate each other.", "role": "assistant" } ]	Thysanozoon nigropapillosum is a species of polyclad flatworms belonging to the family Pseudocerotidae. Some common names include gold-speckled flatworm, marine flatworm, yellow papillae flatworm, yellow-spotted flatworm, and yellow-spotted polyclad flatworm.Thysanozoon nigropapillosum has a long body and broad shape. They grow up to 3 in (76 mm). The dorsal surface is deep black and covered with numerous yellow-tipped papillae varying in size. The ventral surface is dark brown. The outer margin of the body is slightly wavy and bordered in opaque white. They have small, ear-like pseudotentacles in the middle of the anterior end. They swim by propelling themselves through the water with a rhythmic undulating motion of the body.This species is widespread in the tropical Indo-Pacific.Thysanozoon nigropapillosum is quite common along the external reef in the shallow sub-tidal zone. It can swim by undulating and rhythmically contracting the body margins. It feeds on tunicates, using its mouth and large pharynx to engulf Didemnum spp., and later regurgitates food pellets containing the calcareous spicules present in their tunics. Polyclad flatworms are hermaphrodites, with each one of a pair of flatworms trying to inseminate the other. Transfer of sperm may be by hypodermic insemination, but this is made difficult in this species by the presence of the papillae, and dermal impregnation often occurs. This involves sperm entering the body directly after being placed in the marginal region of the skin. Such dermal insemination is facilitated by the fact that the flatworm has two penises which are strengthened by the presence of rhabdites, and these can grip the margin of the partner and keep hold, even while the recipient is swimming.World Database of Marine SpeciesSeaslugsWormsZipcodezooGwannonPhotos of Thysanozoon nigropapillosum on Sealife Collection Please summarize what Thysanozoon nigropapillosum are
general_qa	[ { "content": "For Bronze layer, how do you suggest handling the data sources that are coming as JSON/CSV/Avro, etc. (Onprem SQL data imported as json via Azure Data Factory into ADLS Or API Calls that return JSON) Should that be stored without processing in bronze layer in ADLS/Process them and store as Delta in bronze or should we have a pre bronze layer?", "role": "user" }, { "content": "Either method will work. There are some advantages to using a delta bronze layer over relying on your JSON files in ADLS. It is a lot more performant to re-process data that's in the bronze delta format than re-processing the json files in ADLS. This is because all of your data will be consolidated and filesizes will be optimized in your bronze layer.", "role": "assistant" } ]	For Bronze layer, how do you suggest handling the data sources that are coming as JSON/CSV/Avro, etc. (Onprem SQL data imported as json via Azure Data Factory into ADLS Or API Calls that return JSON) Should that be stored without processing in bronze layer in ADLS/Process them and store as Delta in bronze or should we have a pre bronze layer?
general_qa	[ { "content": "What is a mulligan in Magic the Gathering?", "role": "user" }, { "content": "A mulligan in Magic the Gathering is an action you can take if you are not happy with your opening hand in a game. Depending on the format you play, different rules apply. For competitive one versus one formats, the rule is that you draw an initial hand of seven cards. If you are unhappy with the hand, you can shuffle it back in your deck, and again draw a hand of seven cards. If you are then happy with the hand, you can keep it but you have to choose and put one card from your hand and put it on the bottom of the deck. If you are unhappy, you can mulligan again, but then you'll have to put back two cards, and so forth.", "role": "assistant" } ]	What is a mulligan in Magic the Gathering?
summarization	[ { "content": "In 1948 Gordon was 3rd in the National League in slugging percentage (.537), 4th in RBIs (107), 5th in home runs (30; a career high) and at-bats per home run (17.4), 6th in runs (100; a career high) and total bases (280), 8th in batting average (.299), 9th in walks (74) and OBP (.390), and 10th in stolen bases (8). He was voted onto the All-Star team for his first time. The Giants held \"Sid Gordon Day\" at the Polo Grounds, and he received a new car, golf clubs, and a set of luggage. July 3 was also dubbed \"Sid Gordon Day\" at Ebbets Field, marking a rare honor for a visiting player. He finished 4th in voting for the 1948 National League MVP. Which major league baseball season is considered Sid Gordon's breakout year?", "role": "user" }, { "content": "Sid Gordon during his second stint with the New York Giants in 1948 had one of the best all around hitting performance seasons in mayor league baseball history. Gordon finished in the top ten in ten National League offensive performance statistics including third in slugging percentage (.537), 4th in RBIs (107), 5th in home runs (30; a career high) and at-bats per home run (17.4), 6th in runs (100; a career high) and total bases (280), 8th in batting average (.299), 9th in walks (74) and OBP (.390), and 10th in stolen bases (8). Sid Gordon was also voted to the all-star team and finished 4th in voting for the National League most valuable player (MVP) award.", "role": "assistant" } ]	In 1948 Gordon was 3rd in the National League in slugging percentage (.537), 4th in RBIs (107), 5th in home runs (30; a career high) and at-bats per home run (17.4), 6th in runs (100; a career high) and total bases (280), 8th in batting average (.299), 9th in walks (74) and OBP (.390), and 10th in stolen bases (8). He was voted onto the All-Star team for his first time. The Giants held "Sid Gordon Day" at the Polo Grounds, and he received a new car, golf clubs, and a set of luggage. July 3 was also dubbed "Sid Gordon Day" at Ebbets Field, marking a rare honor for a visiting player. He finished 4th in voting for the 1948 National League MVP. Which major league baseball season is considered Sid Gordon's breakout year?
general_qa	[ { "content": "The French flair is the way the French see life, it has to be grandiose in the sense that even the small thing can be beautiful, stylish, and unique. There is a special French flair in Rugby, where it comes with the sense that anything can happen, even the more desperate attack would be fully supported and played like a final rally, full of bravery. This type of behavior is feared as it is based on improvisation, and has been in the past very efficient even against the very best.", "role": "user" }, { "content": "What is the French Flair?", "role": "assistant" } ]	The French flair is the way the French see life, it has to be grandiose in the sense that even the small thing can be beautiful, stylish, and unique. There is a special French flair in Rugby, where it comes with the sense that anything can happen, even the more desperate attack would be fully supported and played like a final rally, full of bravery. This type of behavior is feared as it is based on improvisation, and has been in the past very efficient even against the very best.
open_qa	[ { "content": "Are the Golden State Warriors a good basketball team?", "role": "user" }, { "content": "The Golden State Warriors won the most recent NBA championship in 2022. In the last 8 years they reached the NBA finals 6 times, won 4 championships, and set the record for regular seasons wins with 73. Stephen Curry, the Warriors best player, also won the NBA Most Valuable Player award twice during that same span. This success is generally regarded as a dynasty, a term used to describe a dominating team over a long period.\n\nHowever, this season the Warriors have struggled. Despite winning the most recent NBA championship only 9 months ago, near the end of this regular season they are only a few games above .500. Their current record is 42-38 as of today, which is only the 13th best record in the NBA out of 30 teams.\n\nThe reason for the drop is not clear. The Warriors have the same starting lineup as last year with 4 All-Stars and when playing at Home their record is among the best in the league. However, when playing on the road the Warriors have one of the worst records at 9-30. A big factor in their road losses is lack of good defense, as opposing teams score considerably more points against the Warriors than when the Warriors play at home. There seems to be a lack of focus and intensity from the Warriors players on defense in these away games and in the NBA, where the margin between winning and losing is very thin, that has seemed to make the difference.", "role": "assistant" } ]	Are the Golden State Warriors a good basketball team?
information_extraction	[ { "content": "In common usage, climate change describes global warming—the ongoing increase in global average temperature—and its effects on Earth's climate system. Climate change in a broader sense also includes previous long-term changes to Earth's climate. The current rise in global average temperature is more rapid than previous changes, and is primarily caused by humans burning fossil fuels. Fossil fuel use, deforestation, and some agricultural and industrial practices increase greenhouse gases, notably carbon dioxide and methane. Greenhouse gases absorb some of the heat that the Earth radiates after it warms from sunlight. Larger amounts of these gases trap more heat in Earth's lower atmosphere, causing global warming.\n\nDue to climate change, deserts are expanding, while heat waves and wildfires are becoming more common. Increased warming in the Arctic has contributed to melting permafrost, glacial retreat and sea ice loss. Higher temperatures are also causing more intense storms, droughts, and other weather extremes. Rapid environmental change in mountains, coral reefs, and the Arctic is forcing many species to relocate or become extinct. Even if efforts to minimise future warming are successful, some effects will continue for centuries. These include ocean heating, ocean acidification and sea level rise. From the Passage please find out what are the cause of climate change", "role": "user" }, { "content": "1. Climate change is primarily caused by humans burning fossil fuels.\n2. Fossil fuel use, \n3. deforestation,\n4.agricultural and industrial practices increase", "role": "assistant" } ]	In common usage, climate change describes global warming—the ongoing increase in global average temperature—and its effects on Earth's climate system. Climate change in a broader sense also includes previous long-term changes to Earth's climate. The current rise in global average temperature is more rapid than previous changes, and is primarily caused by humans burning fossil fuels. Fossil fuel use, deforestation, and some agricultural and industrial practices increase greenhouse gases, notably carbon dioxide and methane. Greenhouse gases absorb some of the heat that the Earth radiates after it warms from sunlight. Larger amounts of these gases trap more heat in Earth's lower atmosphere, causing global warming. Due to climate change, deserts are expanding, while heat waves and wildfires are becoming more common. Increased warming in the Arctic has contributed to melting permafrost, glacial retreat and sea ice loss. Higher temperatures are also causing more intense storms, droughts, and other weather extremes. Rapid environmental change in mountains, coral reefs, and the Arctic is forcing many species to relocate or become extinct. Even if efforts to minimise future warming are successful, some effects will continue for centuries. These include ocean heating, ocean acidification and sea level rise. From the Passage please find out what are the cause of climate change
general_qa	[ { "content": "What programming language should I learn?", "role": "user" }, { "content": "There are many factors to consider when choosing a programming language including ease of use, support by the developer community, licensing, and your application. Programming languages known for their readability include languages like Python and Go. These languages also have open source licenses with strong support by the developer community for various libraries one can include in their applications. Languages with a REPL are often easier to start learning because they’re interactive. Java, C, C++, and Scala are common languages but are often considered to be more challenging to start with than a scripting and general purpose language like Python. For specific applications like web pages, JavaScript is used by most websites.", "role": "assistant" } ]	What programming language should I learn?
general_qa	[ { "content": "What are Arabic numerals?", "role": "user" }, { "content": "Arabic numerals refers to the ten digits commonly used today to represent numbers. The digits are 0, 1, 2, 3, 4, 5, 6, 7, 8, 9. They were adopted by European mathematicians around the 10th century C.E. from numerals used by Arabic speakers in North Africa. The numerals are used in a positional or place-value notation which makes complex calculations by humans easier.", "role": "assistant" } ]	What are Arabic numerals?
summarization	[ { "content": "Unanimous is a United Kingdom-based game show broadcast on Channel 4 from 27 October 2006 to 15 December 2006. It was based on an American game show titled Unan1mous. What is Unanimous?", "role": "user" }, { "content": "Unanimous is a United Kingdom-based game show broadcast on Channel 4 from 27 October 2006 to 15 December 2006. It was based on an American game show titled Unan1mous.\n\nUnanimous: The Fallout, was shown on E4 later at night after Unanimous. It was hosted by comedians Paddy McGuinness and Olivia Lee and featured regular guest Karl Daly, played by Tom Bennett.\n\nThe 'Host' of the show was Alex Humes. Although fairly unknown, Humes starred in the reality TV programme Space Cadets, also for Channel 4, where he played one of the Russian pilots. The voice-over for the programme was provided by Phil Gallagher.", "role": "assistant" } ]	Unanimous is a United Kingdom-based game show broadcast on Channel 4 from 27 October 2006 to 15 December 2006. It was based on an American game show titled Unan1mous. What is Unanimous?
general_qa	[ { "content": "While deciding to buy a bicycle, you need to consider few parameters such as age, gender, price, quality, the purpose of buying, type of bicycle/features etc. For ex: if you are a boy of age 24, then you would consider to buy a bicycle with height of 18\" inch, with gear & shocker, etc.\nAnd if you are a girl of age 6 yrs, then you would consider a kid friendly bicycle with good safety features that the kid can ride.", "role": "user" }, { "content": "Budget, quality and the person who will ride the bicycle are key thing. Start with exploring the options based on budget and the type of bicycle that you may need and then narrow down the bicycle basis your requirement. And then compare with features, price and quality that can help to narrow down the selection process.", "role": "assistant" } ]	While deciding to buy a bicycle, you need to consider few parameters such as age, gender, price, quality, the purpose of buying, type of bicycle/features etc. For ex: if you are a boy of age 24, then you would consider to buy a bicycle with height of 18" inch, with gear & shocker, etc. And if you are a girl of age 6 yrs, then you would consider a kid friendly bicycle with good safety features that the kid can ride.
general_qa	[ { "content": "What is a good anime to watch for someone who has never watched anime before?", "role": "user" }, { "content": "This very much depends on on your personal preferences, but I’ll give you a couple of options that I think would be good starter animes. One of the most famous and highly acclaimed anime is Cowboy Bepop, which is a genre-crossing single-season show about a crew of bounty hunters aboard a spaceship called the Bepop. Another popular entry into anime is One Punch Man, which is an action/comedy about a superhero who can defeat anyone with one punch, leading him to grow bored and search for meaning and challenge.", "role": "assistant" } ]	What is a good anime to watch for someone who has never watched anime before?
summarization	[ { "content": "the famous professor Johann Bernoulli made it a special pleasure for himself to help me along in the mathematical sciences. Private lessons, however, he refused because of his busy schedule. However, he gave me a far more salutary advice, which consisted in myself getting a hold of some of the more difficult mathematical books and working through them with great diligence, and should I encounter some objections or difficulties, he offered me free access to him every Saturday afternoon, and he was gracious enough to comment on the collected difficulties, which was done with such a desired advantage that, when he resolved one of my objections, ten others at once disappeared, which certainly is the best method of making happy progress in the mathematical sciences. According to the passage what is the best way of making progress with mathematics?", "role": "user" }, { "content": "Working through the more difficult mathematical books with great diligence, collect questions and difficulties and seek help from a professor to solve them", "role": "assistant" } ]	the famous professor Johann Bernoulli made it a special pleasure for himself to help me along in the mathematical sciences. Private lessons, however, he refused because of his busy schedule. However, he gave me a far more salutary advice, which consisted in myself getting a hold of some of the more difficult mathematical books and working through them with great diligence, and should I encounter some objections or difficulties, he offered me free access to him every Saturday afternoon, and he was gracious enough to comment on the collected difficulties, which was done with such a desired advantage that, when he resolved one of my objections, ten others at once disappeared, which certainly is the best method of making happy progress in the mathematical sciences. According to the passage what is the best way of making progress with mathematics?
open_qa	[ { "content": "When was the last time aliens visited the earth?", "role": "user" }, { "content": "There is no evidence that aliens have ever visited the earth. There have been many claims including UFO sightings, strange atmospheric patterns, ancient buildings showing characteristics outside of human capabilities at that time. But all of these have been refuted and there is no credible evidence of alien visitation.", "role": "assistant" } ]	When was the last time aliens visited the earth?
open_qa	[ { "content": "Name the place in New Zealand, where a volcano erupted?", "role": "user" }, { "content": "Whakaari (also known as White Island)", "role": "assistant" } ]	Name the place in New Zealand, where a volcano erupted?
summarization	[ { "content": "A civil war or intrastate war is a war between organized groups within the same state (or country). The aim of one side may be to take control of the country or a region, to achieve independence for a region, or to change government policies. The term is a calque of Latin bellum civile which was used to refer to the various civil wars of the Roman Republic in the 1st century BC. Write down some important aspects regarding civil war from the given text", "role": "user" }, { "content": "1. An intrastate or civil war is a conflict between organised parties fighting within a single state. (or country).\n2. One side may want to overthrow the government, seize control of an area, secure the independence of a region, or alter public policy.\n3. The phrase is a calque of the Latin phrase bellum civile, which was used to describe the numerous civil wars that raged throughout the Roman Republic in the first century BC.", "role": "assistant" } ]	A civil war or intrastate war is a war between organized groups within the same state (or country). The aim of one side may be to take control of the country or a region, to achieve independence for a region, or to change government policies. The term is a calque of Latin bellum civile which was used to refer to the various civil wars of the Roman Republic in the 1st century BC. Write down some important aspects regarding civil war from the given text
brainstorming	[ { "content": "What are some common bases for tacos?", "role": "user" }, { "content": "Tacos are typically built on top of a soft wheat tortilla, soft corn tortilla, hard taco shell, lettuce, or a baked taco shell.", "role": "assistant" } ]	What are some common bases for tacos?
summarization	[ { "content": "Due to climate change, deserts are expanding, while heat waves and wildfires are becoming more common.Increased warming in the Arctic has contributed to melting permafrost, glacial retreat and sea ice loss. Higher temperatures are also causing more intense storms, droughts, and other weather extremes. Rapid environmental change in mountains, coral reefs, and the Arctic is forcing many species to relocate or become extinct.Even if efforts to minimise future warming are successful, some effects will continue for centuries. These include ocean heating, ocean acidification and sea level rise.\nClimate change threatens people with increased flooding, extreme heat, increased food and water scarcity, more disease, and economic loss. Human migration and conflict can also be a result. What are the main impacts to the earth as a result of climate change.", "role": "user" }, { "content": "More intense storms and droughts; Heat waves and wild fires are more common; Extinction of animal species; Increased food and water scarcity", "role": "assistant" } ]	Due to climate change, deserts are expanding, while heat waves and wildfires are becoming more common.Increased warming in the Arctic has contributed to melting permafrost, glacial retreat and sea ice loss. Higher temperatures are also causing more intense storms, droughts, and other weather extremes. Rapid environmental change in mountains, coral reefs, and the Arctic is forcing many species to relocate or become extinct.Even if efforts to minimise future warming are successful, some effects will continue for centuries. These include ocean heating, ocean acidification and sea level rise. Climate change threatens people with increased flooding, extreme heat, increased food and water scarcity, more disease, and economic loss. Human migration and conflict can also be a result. What are the main impacts to the earth as a result of climate change.
summarization	[ { "content": "Barack Hussein Obama II (/bəˈrɑːk huːˈseɪn oʊˈbɑːmə/ (listen) bə-RAHK hoo-SAYN oh-BAH-mə; born August 4, 1961) is an American former politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, he was the first African-American president of the United States. Obama previously served as a U.S. senator representing Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004, and worked as a civil rights lawyer before holding public office.\n\nObama was born in Honolulu, Hawaii. After graduating from Columbia University in 1983, he worked as a community organizer in Chicago. In 1988, he enrolled in Harvard Law School, where he was the first black president of the Harvard Law Review. After graduating, he became a civil rights attorney and an academic, teaching constitutional law at the University of Chicago Law School from 1992 to 2004. Turning to elective politics, he represented the 13th district in the Illinois Senate from 1997 until 2004, when he ran for the U.S. Senate. Obama received national attention in 2004 with his March Senate primary win, his well-received keynote address at the July Democratic National Convention, and his landslide November election to the Senate. In 2008, after a close primary campaign against Hillary Clinton, he was nominated by the Democratic Party for president and chose Joe Biden as his running mate. Obama was elected over Republican nominee John McCain in the presidential election and was inaugurated on January 20, 2009. Nine months later, he was named the 2009 Nobel Peace Prize laureate, a decision that drew a mixture of praise and criticism.\n\nObama's first-term actions addressed the global financial crisis and included a major stimulus package, a partial extension of George W. Bush's tax cuts, legislation to reform health care, a major financial regulation reform bill, and the end of a major US military presence in Iraq. Obama also appointed Supreme Court justices Sonia Sotomayor and Elena Kagan, the former being the first Hispanic American on the Supreme Court. He ordered the counterterrorism raid which killed Osama bin Laden and downplayed Bush's counterinsurgency model, expanding air strikes and making extensive use of special forces while encouraging greater reliance on host-government militaries. Can you list a few cities that Obama lived in from the below text?", "role": "user" }, { "content": "Honolulu, Chicago,", "role": "assistant" } ]	Barack Hussein Obama II (/bəˈrɑːk huːˈseɪn oʊˈbɑːmə/ (listen) bə-RAHK hoo-SAYN oh-BAH-mə; born August 4, 1961) is an American former politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, he was the first African-American president of the United States. Obama previously served as a U.S. senator representing Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004, and worked as a civil rights lawyer before holding public office. Obama was born in Honolulu, Hawaii. After graduating from Columbia University in 1983, he worked as a community organizer in Chicago. In 1988, he enrolled in Harvard Law School, where he was the first black president of the Harvard Law Review. After graduating, he became a civil rights attorney and an academic, teaching constitutional law at the University of Chicago Law School from 1992 to 2004. Turning to elective politics, he represented the 13th district in the Illinois Senate from 1997 until 2004, when he ran for the U.S. Senate. Obama received national attention in 2004 with his March Senate primary win, his well-received keynote address at the July Democratic National Convention, and his landslide November election to the Senate. In 2008, after a close primary campaign against Hillary Clinton, he was nominated by the Democratic Party for president and chose Joe Biden as his running mate. Obama was elected over Republican nominee John McCain in the presidential election and was inaugurated on January 20, 2009. Nine months later, he was named the 2009 Nobel Peace Prize laureate, a decision that drew a mixture of praise and criticism. Obama's first-term actions addressed the global financial crisis and included a major stimulus package, a partial extension of George W. Bush's tax cuts, legislation to reform health care, a major financial regulation reform bill, and the end of a major US military presence in Iraq. Obama also appointed Supreme Court justices Sonia Sotomayor and Elena Kagan, the former being the first Hispanic American on the Supreme Court. He ordered the counterterrorism raid which killed Osama bin Laden and downplayed Bush's counterinsurgency model, expanding air strikes and making extensive use of special forces while encouraging greater reliance on host-government militaries. Can you list a few cities that Obama lived in from the below text?
closed_qa	[ { "content": "A large language model (LLM) is a language model consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabelled text using self-supervised learning. LLMs emerged around 2018 and perform well at a wide variety of tasks. This has shifted the focus of natural language processing research away from the previous paradigm of training specialized supervised models for specific tasks.\n\nProperties\nThough the term large language model has no formal definition, it often refers to deep learning models having a parameter count on the order of billions or more. LLMs are general purpose models which excel at a wide range of tasks, as opposed to being trained for one specific task (such as sentiment analysis, named entity recognition, or mathematical reasoning). The skill with which they accomplish tasks, and the range of tasks at which they are capable, seems to be a function of the amount of resources (data, parameter-size, computing power) devoted to them, in a way that is not dependent on additional breakthroughs in design.\n\nThough trained on simple tasks along the lines of predicting the next word in a sentence, neural language models with sufficient training and parameter counts are found to capture much of the syntax and semantics of human language. In addition, large language models demonstrate considerable general knowledge about the world, and are able to \"memorize\" a great quantity of facts during training.\n\nHallucinations\nMain article: Hallucination (artificial intelligence)\nIn artificial intelligence in general, and in large language models in particular, a \"hallucination\" is a confident response that does not seem to be justified by the model's training data.\n\nEmergent abilities\n\nOn a number of natural language benchmarks involving tasks such as question answering, models perform no better than random chance until they reach a certain scale (in this case, measured by training computation), at which point their performance sharply increases. These are examples of emergent abilities.\nUnpredictable abilities that have been observed in large language models but that were not present in simpler models (and that were not explicitly designed into the model) are usually called \"emergent abilities\". Researchers note that such abilities \"cannot be predicted simply by extrapolating the performance of smaller models\". These abilities are discovered rather than programmed-in or designed, in some cases only after the LLM has been publicly deployed. Hundreds of emergent abilities have been described. Examples include multi-step arithmetic, taking college-level exams, identifying the intended meaning of a word, chain-of-thought prompting, decoding the International Phonetic Alphabet, unscrambling a word’s letters, identifying offensive content in paragraphs of Hinglish (a combination of Hindi and English), and generating a similar English equivalent of Kiswahili proverbs.\n\nArchitecture and training\nLarge language models have most commonly used the transformer architecture, which, since 2018, has become the standard deep learning technique for sequential data (previously, recurrent architectures such as the LSTM were most common). LLMs are trained in an unsupervised manner on unannotated text. A left-to-right transformer is trained to maximize the probability assigned to the next word in the training data, given the previous context. Alternatively, an LLM may use a bidirectional transformer (as in the example of BERT), which assigns a probability distribution over words given access to both preceding and following context. In addition to the task of predicting the next word or \"filling in the blanks\", LLMs may be trained on auxiliary tasks which test their understanding of the data distribution such as Next Sentence Prediction (NSP), in which pairs of sentences are presented and the model must predict whether they appear side-by-side in the training corpus.\n\nThe earliest LLMs were trained on corpora having on the order of billions of words. The first model in OpenAI's GPT series was trained in 2018 on BookCorpus, consisting of 985 million words. In the same year, BERT was trained on a combination of BookCorpus and English Wikipedia, totalling 3.3 billion words. In the years since then, training corpora for LLMs have increased by orders of magnitude, reaching up to hundreds of billions or trillions of tokens.\n\nLLMs are computationally expensive to train. A 2020 study estimated the cost of training a 1.5 billion parameter model (1-2 orders of magnitude smaller than the state of the art at the time) at $1.6 million.\n\nA 2020 analysis found that neural language models' capability (as measured by training loss) increased smoothly in a power law relationship with number of parameters, quantity of training data, and computation used for training. These relationships were tested over a wide range of values (up to seven orders of magnitude) and no attenuation of the relationship was observed at the highest end of the range (including for network sizes up to trillions of parameters).\n\nApplication to downstream tasks\nBetween 2018 and 2020, the standard method for harnessing an LLM for a specific natural language processing (NLP) task was to fine tune the model with additional task-specific training. It has subsequently been found that more powerful LLMs such as GPT-3 can solve tasks without additional training via \"prompting\" techniques, in which the problem to be solved is presented to the model as a text prompt, possibly with some textual examples of similar problems and their solutions.\n\nFine-tuning\nMain article: Fine-tuning (machine learning)\nFine-tuning is the practice of modifying an existing pretrained language model by training it (in a supervised fashion) on a specific task (e.g. sentiment analysis, named entity recognition, or part-of-speech tagging). It is a form of transfer learning. It generally involves the introduction of a new set of weights connecting the final layer of the language model to the output of the downstream task. The original weights of the language model may be \"frozen\", such that only the new layer of weights connecting them to the output are learned during training. Alternatively, the original weights may receive small updates (possibly with earlier layers frozen).\n\nPrompting\nSee also: Prompt engineering and Few-shot learning (natural language processing)\nIn the prompting paradigm, popularized by GPT-3, the problem to be solved is formulated via a text prompt, which the model must solve by providing a completion (via inference). In \"few-shot prompting\", the prompt includes a small number of examples of similar (problem, solution) pairs. For example, a sentiment analysis task of labelling the sentiment of a movie review could be prompted as follows:\n\nReview: This movie stinks.\nSentiment: negative\n\nReview: This movie is fantastic!\nSentiment:\n\nIf the model outputs \"positive\", then it has correctly solved the task. In zero-shot prompting, no solve examples are provided. An example of a zero-shot prompt for the same sentiment analysis task would be \"The sentiment associated with the movie review 'This movie is fantastic!' is\".\n\nFew-shot performance of LLMs has been shown to achieve competitive results on NLP tasks, sometimes surpassing prior state-of-the-art fine-tuning approaches. Examples of such NLP tasks are translation, question answering, cloze tasks, unscrambling words, and using a novel word in a sentence. The creation and optimisation of such prompts is called prompt engineering.\n\nInstruction tuning\nInstruction tuning is a form of fine-tuning designed to facilitate more natural and accurate zero-shot prompting interactions. Given a text input, a pretrained language model will generate a completion which matches the distribution of text on which it was trained. A naive language model given the prompt \"Write an essay about the main themes of Hamlet.\" might provide a completion such as \"A late penalty of 10% per day will be applied to submissions received after March 17.\" In instruction tuning, the language model is trained on many examples of tasks formulated as natural language instructions, along with appropriate responses. Various techniques for instruction tuning have been applied in practice. OpenAI's InstructGPT protocol involves supervised fine-tuning on a dataset of human-generated (prompt, response) pairs, followed by reinforcement learning from human feedback (RLHF), in which a reward function was learned based on a dataset of human preferences. Another technique, \"self-instruct\", fine-tunes the language model on a training set of examples which are themselves generated by an LLM (bootstrapped from a small initial set of human-generated examples).\n\nhttps://en.wikipedia.org/wiki/Large_language_model Given these paragraphs about Large language models, what are \"emergent abilities\"?", "role": "user" }, { "content": "Unpredictable abilities that have been observed in large language models but that were not present in simpler models (and that were not explicitly designed into the model) are usually called \"emergent abilities\". Researchers note that such abilities \"cannot be predicted simply by extrapolating the performance of smaller models\". These abilities are discovered rather than programmed-in or designed, in some cases only after the LLM has been publicly deployed.", "role": "assistant" } ]	A large language model (LLM) is a language model consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabelled text using self-supervised learning. LLMs emerged around 2018 and perform well at a wide variety of tasks. This has shifted the focus of natural language processing research away from the previous paradigm of training specialized supervised models for specific tasks. Properties Though the term large language model has no formal definition, it often refers to deep learning models having a parameter count on the order of billions or more. LLMs are general purpose models which excel at a wide range of tasks, as opposed to being trained for one specific task (such as sentiment analysis, named entity recognition, or mathematical reasoning). The skill with which they accomplish tasks, and the range of tasks at which they are capable, seems to be a function of the amount of resources (data, parameter-size, computing power) devoted to them, in a way that is not dependent on additional breakthroughs in design. Though trained on simple tasks along the lines of predicting the next word in a sentence, neural language models with sufficient training and parameter counts are found to capture much of the syntax and semantics of human language. In addition, large language models demonstrate considerable general knowledge about the world, and are able to "memorize" a great quantity of facts during training. Hallucinations Main article: Hallucination (artificial intelligence) In artificial intelligence in general, and in large language models in particular, a "hallucination" is a confident response that does not seem to be justified by the model's training data. Emergent abilities On a number of natural language benchmarks involving tasks such as question answering, models perform no better than random chance until they reach a certain scale (in this case, measured by training computation), at which point their performance sharply increases. These are examples of emergent abilities. Unpredictable abilities that have been observed in large language models but that were not present in simpler models (and that were not explicitly designed into the model) are usually called "emergent abilities". Researchers note that such abilities "cannot be predicted simply by extrapolating the performance of smaller models". These abilities are discovered rather than programmed-in or designed, in some cases only after the LLM has been publicly deployed. Hundreds of emergent abilities have been described. Examples include multi-step arithmetic, taking college-level exams, identifying the intended meaning of a word, chain-of-thought prompting, decoding the International Phonetic Alphabet, unscrambling a word’s letters, identifying offensive content in paragraphs of Hinglish (a combination of Hindi and English), and generating a similar English equivalent of Kiswahili proverbs. Architecture and training Large language models have most commonly used the transformer architecture, which, since 2018, has become the standard deep learning technique for sequential data (previously, recurrent architectures such as the LSTM were most common). LLMs are trained in an unsupervised manner on unannotated text. A left-to-right transformer is trained to maximize the probability assigned to the next word in the training data, given the previous context. Alternatively, an LLM may use a bidirectional transformer (as in the example of BERT), which assigns a probability distribution over words given access to both preceding and following context. In addition to the task of predicting the next word or "filling in the blanks", LLMs may be trained on auxiliary tasks which test their understanding of the data distribution such as Next Sentence Prediction (NSP), in which pairs of sentences are presented and the model must predict whether they appear side-by-side in the training corpus. The earliest LLMs were trained on corpora having on the order of billions of words. The first model in OpenAI's GPT series was trained in 2018 on BookCorpus, consisting of 985 million words. In the same year, BERT was trained on a combination of BookCorpus and English Wikipedia, totalling 3.3 billion words. In the years since then, training corpora for LLMs have increased by orders of magnitude, reaching up to hundreds of billions or trillions of tokens. LLMs are computationally expensive to train. A 2020 study estimated the cost of training a 1.5 billion parameter model (1-2 orders of magnitude smaller than the state of the art at the time) at $1.6 million. A 2020 analysis found that neural language models' capability (as measured by training loss) increased smoothly in a power law relationship with number of parameters, quantity of training data, and computation used for training. These relationships were tested over a wide range of values (up to seven orders of magnitude) and no attenuation of the relationship was observed at the highest end of the range (including for network sizes up to trillions of parameters). Application to downstream tasks Between 2018 and 2020, the standard method for harnessing an LLM for a specific natural language processing (NLP) task was to fine tune the model with additional task-specific training. It has subsequently been found that more powerful LLMs such as GPT-3 can solve tasks without additional training via "prompting" techniques, in which the problem to be solved is presented to the model as a text prompt, possibly with some textual examples of similar problems and their solutions. Fine-tuning Main article: Fine-tuning (machine learning) Fine-tuning is the practice of modifying an existing pretrained language model by training it (in a supervised fashion) on a specific task (e.g. sentiment analysis, named entity recognition, or part-of-speech tagging). It is a form of transfer learning. It generally involves the introduction of a new set of weights connecting the final layer of the language model to the output of the downstream task. The original weights of the language model may be "frozen", such that only the new layer of weights connecting them to the output are learned during training. Alternatively, the original weights may receive small updates (possibly with earlier layers frozen). Prompting See also: Prompt engineering and Few-shot learning (natural language processing) In the prompting paradigm, popularized by GPT-3, the problem to be solved is formulated via a text prompt, which the model must solve by providing a completion (via inference). In "few-shot prompting", the prompt includes a small number of examples of similar (problem, solution) pairs. For example, a sentiment analysis task of labelling the sentiment of a movie review could be prompted as follows: Review: This movie stinks. Sentiment: negative Review: This movie is fantastic! Sentiment: If the model outputs "positive", then it has correctly solved the task. In zero-shot prompting, no solve examples are provided. An example of a zero-shot prompt for the same sentiment analysis task would be "The sentiment associated with the movie review 'This movie is fantastic!' is". Few-shot performance of LLMs has been shown to achieve competitive results on NLP tasks, sometimes surpassing prior state-of-the-art fine-tuning approaches. Examples of such NLP tasks are translation, question answering, cloze tasks, unscrambling words, and using a novel word in a sentence. The creation and optimisation of such prompts is called prompt engineering. Instruction tuning Instruction tuning is a form of fine-tuning designed to facilitate more natural and accurate zero-shot prompting interactions. Given a text input, a pretrained language model will generate a completion which matches the distribution of text on which it was trained. A naive language model given the prompt "Write an essay about the main themes of Hamlet." might provide a completion such as "A late penalty of 10% per day will be applied to submissions received after March 17." In instruction tuning, the language model is trained on many examples of tasks formulated as natural language instructions, along with appropriate responses. Various techniques for instruction tuning have been applied in practice. OpenAI's InstructGPT protocol involves supervised fine-tuning on a dataset of human-generated (prompt, response) pairs, followed by reinforcement learning from human feedback (RLHF), in which a reward function was learned based on a dataset of human preferences. Another technique, "self-instruct", fine-tunes the language model on a training set of examples which are themselves generated by an LLM (bootstrapped from a small initial set of human-generated examples). https://en.wikipedia.org/wiki/Large_language_model Given these paragraphs about Large language models, what are "emergent abilities"?