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Which is the best place to Kayak in Austin?		Lady Bird Lake in Austin downtown is revered to be the best place to go Kayaking. Paddlers can choose to head west from the Rowing Dock to take in nature views. But a small paddle down the river to the east will greet paddlers to magnificent downtown skyline views.	open_qa	<START_INST>Which is the best place to Kayak in Austin?<END_INST><START_CTX><END_CTX><START_A>Lady Bird Lake in Austin downtown is revered to be the best place to go Kayaking. Paddlers can choose to head west from the Rowing Dock to take in nature views. But a small paddle down the river to the east will greet paddlers to magnificent downtown skyline views.<END_A>
What does democratizing data mean?		Making data accessible to all people and educating people on how to work with data, no matter their technical background.	open_qa	<START_INST>What does democratizing data mean?<END_INST><START_CTX><END_CTX><START_A>Making data accessible to all people and educating people on how to work with data, no matter their technical background.<END_A>
Find the bird in this list: Shark, Dog, Arctic tern		Arctic tern	classification	<START_INST>Find the bird in this list: Shark, Dog, Arctic tern<END_INST><START_CTX><END_CTX><START_A>Arctic tern<END_A>
Classify each of the following as either software or hardware: iPhone, iOS 13, Windows 10, Macbook, Windows Server 2012, iPad, Windows 11		iPhone: Hardware iOS 13: Software Windows 10: Software Macbook: Hardware Windows Server 2012: Software iPad: Hardware Windows 11: Software	classification	<START_INST>Classify each of the following as either software or hardware: iPhone, iOS 13, Windows 10, Macbook, Windows Server 2012, iPad, Windows 11<END_INST><START_CTX><END_CTX><START_A>iPhone: Hardware iOS 13: Software Windows 10: Software Macbook: Hardware Windows Server 2012: Software iPad: Hardware Windows 11: Software<END_A>
Few creative ways we can market products and services in modern era?		1. Influencer Partnership 2. Social Media Campaign 3. Events and experiential marketing	brainstorming	<START_INST>Few creative ways we can market products and services in modern era?<END_INST><START_CTX><END_CTX><START_A>1. Influencer Partnership 2. Social Media Campaign 3. Events and experiential marketing<END_A>
How do the definitions of "first-degree relative" and "nuclear family" differ?	A first-degree relative (FDR) is a person's parent (father or mother), full sibling (brother or sister) or child. It constitutes a category of family members that largely overlaps with the term nuclear family, but without spouses.	Nuclear family includes spouses while first-degree relatives are only blood related.	closed_qa	<START_INST>How do the definitions of "first-degree relative" and "nuclear family" differ?<END_INST><START_CTX>A first-degree relative (FDR) is a person's parent (father or mother), full sibling (brother or sister) or child. It constitutes a category of family members that largely overlaps with the term nuclear family, but without spouses.<END_CTX><START_A>Nuclear family includes spouses while first-degree relatives are only blood related.<END_A>
Write an introductory email to a student I will be academically coaching through Services for Students with Disabilities office (SSD).		Hi Naomi, My name is Chandler, and I am your Academic Coach through the Services for Students with Disabilities office (SSD). I am looking forward to working with you during this time in your academic journey! As your academic coach, my role is to partner with you as we focus on your process of learning. During our time together we will examine your: learning experience habits of working current difficulties or barriers to success While examining these areas we will work together to: Develop individualized learning strategies and study habits Navigate campus resources Promote self-advocacy We will also focus on building and sustaining Executive Functioning skills such as: Time Management Organization Self-regulation Self-efficacy I look forward to hearing back from you.	creative_writing	<START_INST>Write an introductory email to a student I will be academically coaching through Services for Students with Disabilities office (SSD).<END_INST><START_CTX><END_CTX><START_A>Hi Naomi, My name is Chandler, and I am your Academic Coach through the Services for Students with Disabilities office (SSD). I am looking forward to working with you during this time in your academic journey! As your academic coach, my role is to partner with you as we focus on your process of learning. During our time together we will examine your: learning experience habits of working current difficulties or barriers to success While examining these areas we will work together to: Develop individualized learning strategies and study habits Navigate campus resources Promote self-advocacy We will also focus on building and sustaining Executive Functioning skills such as: Time Management Organization Self-regulation Self-efficacy I look forward to hearing back from you.<END_A>
Summarize the benefits of installing artificial turf in my backyard instead of real grass		Installation of artificial turf provides a many benefits over natural grass. Maintenance: Artificial turn does not need trimming or mowing. Drought Preparedness: Artificial turn does not require water. You can opt to install some sprinklers for occasional cleaning or cooling purposes, but sprinklers are not necessary. Cleanliness: Artificial turn eliminates dirt and grass stains on those who enjoy it. Year round yard enjoyment: Artificial turn allows you to enjoy lawn activities all year long. No more dead lawn in the summer or muddy patches in the winter. Aesthetics: Artificial turf will always give your yard a dependably green lawn area. Adding surrounding landscape of bark, mulch, shrubs and flowers, helps seamlessly blend the turf into your overall landscape plan.	brainstorming	<START_INST>Summarize the benefits of installing artificial turf in my backyard instead of real grass<END_INST><START_CTX><END_CTX><START_A>Installation of artificial turf provides a many benefits over natural grass. Maintenance: Artificial turn does not need trimming or mowing. Drought Preparedness: Artificial turn does not require water. You can opt to install some sprinklers for occasional cleaning or cooling purposes, but sprinklers are not necessary. Cleanliness: Artificial turn eliminates dirt and grass stains on those who enjoy it. Year round yard enjoyment: Artificial turn allows you to enjoy lawn activities all year long. No more dead lawn in the summer or muddy patches in the winter. Aesthetics: Artificial turf will always give your yard a dependably green lawn area. Adding surrounding landscape of bark, mulch, shrubs and flowers, helps seamlessly blend the turf into your overall landscape plan.<END_A>
What is the difference between race and ethnicity?		Race is a combination of cultural, physical and behavioral attributes whereas ethnicity is recognized by shared cultures and language.	open_qa	<START_INST>What is the difference between race and ethnicity?<END_INST><START_CTX><END_CTX><START_A>Race is a combination of cultural, physical and behavioral attributes whereas ethnicity is recognized by shared cultures and language.<END_A>
What are the benefits of collaborating on a tech blog with your customers?		It will improve visibility of the product. It will help with your customers' career. It will benefit other customers with related use cases.	brainstorming	<START_INST>What are the benefits of collaborating on a tech blog with your customers?<END_INST><START_CTX><END_CTX><START_A>It will improve visibility of the product. It will help with your customers' career. It will benefit other customers with related use cases.<END_A>
Who are the considered to be the best soccer players of all time, and what countries are they from?		Pelé (Brazil), Lionel Messi (Argentina), Cristiano Ronaldo (Portugal), Ronaldo (Brazil), Diego Maradona (Argentina), Franz Beckenbauer (Germany), George Best (Northern Ireland), Michel Platini (France), Johan Cruyff (The Netherlands), and David Beckham (England).	open_qa	<START_INST>Who are the considered to be the best soccer players of all time, and what countries are they from?<END_INST><START_CTX><END_CTX><START_A>Pelé (Brazil), Lionel Messi (Argentina), Cristiano Ronaldo (Portugal), Ronaldo (Brazil), Diego Maradona (Argentina), Franz Beckenbauer (Germany), George Best (Northern Ireland), Michel Platini (France), Johan Cruyff (The Netherlands), and David Beckham (England).<END_A>
Give me a list of six different types of flowers I could buy someone		Carnations, Roses, Tulips, Peonies, Daffodils, Lilies	brainstorming	<START_INST>Give me a list of six different types of flowers I could buy someone<END_INST><START_CTX><END_CTX><START_A>Carnations, Roses, Tulips, Peonies, Daffodils, Lilies<END_A>
What is the Auburn Ski Club?		The Auburn Ski Club is a non-profit training center that owns and operates a lodge/locker room/gym as well as a Nordic ski course and biathlon range adjacent to Boreal Mountain Resort. The club runs alpine, Nordic, snowboard and biathlon training programs for youth athletes from beginner to expert level.	open_qa	<START_INST>What is the Auburn Ski Club?<END_INST><START_CTX><END_CTX><START_A>The Auburn Ski Club is a non-profit training center that owns and operates a lodge/locker room/gym as well as a Nordic ski course and biathlon range adjacent to Boreal Mountain Resort. The club runs alpine, Nordic, snowboard and biathlon training programs for youth athletes from beginner to expert level.<END_A>
How many times did Barton switch parties?	Barton switched parties three times in his political life. He was first elected as a Democratic-Republican; he then switched to an Adams Democrat for his re-election in 1825. When he ran for the Senate again in 1830 he switched to being an Anti-Jacksonian candidate. In the Senate, he served as chairman of the committee on public lands. He was a member of the Missouri State Senate in 1834–1835.	Three times	information_extraction	<START_INST>How many times did Barton switch parties?<END_INST><START_CTX>Barton switched parties three times in his political life. He was first elected as a Democratic-Republican; he then switched to an Adams Democrat for his re-election in 1825. When he ran for the Senate again in 1830 he switched to being an Anti-Jacksonian candidate. In the Senate, he served as chairman of the committee on public lands. He was a member of the Missouri State Senate in 1834–1835.<END_CTX><START_A>Three times<END_A>
Why is wine country in Northern California a great place to travel?		Traveling to wine country in Northern California is an excellent place to travel. There are countless vineyards to visit, some of the best restaurants in the country, and picturesque views everywhere. Visiting wine country is a relaxing vacation where travelers can enjoy slowing down their daily pace and just enjoy the beautiful surroundings around them. Wine country is also rich in history and the quaint towns can be fun to just walk around.	creative_writing	<START_INST>Why is wine country in Northern California a great place to travel?<END_INST><START_CTX><END_CTX><START_A>Traveling to wine country in Northern California is an excellent place to travel. There are countless vineyards to visit, some of the best restaurants in the country, and picturesque views everywhere. Visiting wine country is a relaxing vacation where travelers can enjoy slowing down their daily pace and just enjoy the beautiful surroundings around them. Wine country is also rich in history and the quaint towns can be fun to just walk around.<END_A>
What format does Delta Lake use to store data?		Delta Lake uses versioned Parquet files to store your data in your cloud storage. Apart from the versions, Delta Lake also stores a transaction log to keep track of all the commits made to the table or blob store directory to provide ACID transactions.	general_qa	<START_INST>What format does Delta Lake use to store data?<END_INST><START_CTX><END_CTX><START_A>Delta Lake uses versioned Parquet files to store your data in your cloud storage. Apart from the versions, Delta Lake also stores a transaction log to keep track of all the commits made to the table or blob store directory to provide ACID transactions.<END_A>
Did the Bisons have a winning season in 1958?	The 1958 North Dakota State Bison football team was an American football team that represented North Dakota State University during the 1958 NCAA College Division football season as a member of the North Central Conference. In their second year under head coach Bob Danielson, the team compiled a 3–6 record.	No, the Bisons had a losing season in 1958, only winning 3 of their 9 games and losing 6.	summarization	<START_INST>Did the Bisons have a winning season in 1958?<END_INST><START_CTX>The 1958 North Dakota State Bison football team was an American football team that represented North Dakota State University during the 1958 NCAA College Division football season as a member of the North Central Conference. In their second year under head coach Bob Danielson, the team compiled a 3–6 record.<END_CTX><START_A>No, the Bisons had a losing season in 1958, only winning 3 of their 9 games and losing 6.<END_A>
What are the main components of Apache Spark based on the paragraph?	Apache Spark requires a cluster manager and a distributed storage system. For cluster management, Spark supports standalone (native Spark cluster, where you can launch a cluster either manually or use the launch scripts provided by the install package. It is also possible to run these daemons on a single machine for testing), Hadoop YARN, Apache Mesos or Kubernetes. For distributed storage, Spark can interface with a wide variety, including Alluxio, Hadoop Distributed File System (HDFS), MapR File System (MapR-FS), Cassandra, OpenStack Swift, Amazon S3, Kudu, Lustre file system, or a custom solution can be implemented. Spark also supports a pseudo-distributed local mode, usually used only for development or testing purposes, where distributed storage is not required and the local file system can be used instead; in such a scenario, Spark is run on a single machine with one executor per CPU core.	Based on the paragraph, a cluster manager and a distributed storage are two main components of Apache Spark. For cluster manager, Spark supports a standalone cluster, or Hadoop YARN, Apache Mesos, or Kubernetes. For storage, Spark supports local file systems for development and testing, as well as Hadoop File System, Cassandra, and cloud storages	closed_qa	<START_INST>What are the main components of Apache Spark based on the paragraph?<END_INST><START_CTX>Apache Spark requires a cluster manager and a distributed storage system. For cluster management, Spark supports standalone (native Spark cluster, where you can launch a cluster either manually or use the launch scripts provided by the install package. It is also possible to run these daemons on a single machine for testing), Hadoop YARN, Apache Mesos or Kubernetes. For distributed storage, Spark can interface with a wide variety, including Alluxio, Hadoop Distributed File System (HDFS), MapR File System (MapR-FS), Cassandra, OpenStack Swift, Amazon S3, Kudu, Lustre file system, or a custom solution can be implemented. Spark also supports a pseudo-distributed local mode, usually used only for development or testing purposes, where distributed storage is not required and the local file system can be used instead; in such a scenario, Spark is run on a single machine with one executor per CPU core.<END_CTX><START_A>Based on the paragraph, a cluster manager and a distributed storage are two main components of Apache Spark. For cluster manager, Spark supports a standalone cluster, or Hadoop YARN, Apache Mesos, or Kubernetes. For storage, Spark supports local file systems for development and testing, as well as Hadoop File System, Cassandra, and cloud storages<END_A>
Identify which instrument is string or percussion: Carimbo, Gusle		Carimbo is percussion, Gusle is string.	classification	<START_INST>Identify which instrument is string or percussion: Carimbo, Gusle<END_INST><START_CTX><END_CTX><START_A>Carimbo is percussion, Gusle is string.<END_A>
Whos the lowest drafted NBA player to win the MVP (most valuable player) award?	Each member of the voting panel casts a vote for first to fifth place selections. Each first-place vote is worth 10 points; each second-place vote is worth seven; each third-place vote is worth five, fourth-place is worth three and fifth-place is worth one. Starting from 2010, one ballot was cast by fans through online voting. The player with the highest point total wins the award. As of the 2021–22 season, the current holder of the award is Nikola Jokić of the Denver Nuggets. Jokić is the lowest draft pick to win the award, being selected 41st by Denver in the 2014 NBA draft. Every player who has won this award and has been eligible for the Naismith Memorial Basketball Hall of Fame has been inducted. Kareem Abdul-Jabbar won the award a record six times. He is also the only player to win the award despite his team not making the playoffs back in the 1975–76 season. Both Bill Russell and Michael Jordan won the award five times, while Wilt Chamberlain and LeBron James won the award four times. Russell and James are the only players to have won the award four times in five seasons. Moses Malone, Larry Bird and Magic Johnson each won the award three times, while Bob Pettit, Karl Malone, Tim Duncan, Steve Nash, Stephen Curry, Giannis Antetokounmpo and Nikola Jokić have each won it twice. Russell, Chamberlain, and Bird are the only players to win the award in three consecutive years. Only two rookies have won the award: Chamberlain in the 1959–60 season and Wes Unseld in the 1968–69 season. Hakeem Olajuwon of Nigeria, Duncan of the U.S. Virgin Islands, Nash of Canada, Dirk Nowitzki of Germany, Antetokounmpo of Greece, and Jokić of Serbia are the only MVP winners considered "international players" by the NBA.	Nikola Jokic is the lowest draft pick to win the award. Nikola Jokic was drafted 41st overall by the Denver Nuggets in 2014.	information_extraction	<START_INST>Whos the lowest drafted NBA player to win the MVP (most valuable player) award?<END_INST><START_CTX>Each member of the voting panel casts a vote for first to fifth place selections. Each first-place vote is worth 10 points; each second-place vote is worth seven; each third-place vote is worth five, fourth-place is worth three and fifth-place is worth one. Starting from 2010, one ballot was cast by fans through online voting. The player with the highest point total wins the award. As of the 2021–22 season, the current holder of the award is Nikola Jokić of the Denver Nuggets. Jokić is the lowest draft pick to win the award, being selected 41st by Denver in the 2014 NBA draft. Every player who has won this award and has been eligible for the Naismith Memorial Basketball Hall of Fame has been inducted. Kareem Abdul-Jabbar won the award a record six times. He is also the only player to win the award despite his team not making the playoffs back in the 1975–76 season. Both Bill Russell and Michael Jordan won the award five times, while Wilt Chamberlain and LeBron James won the award four times. Russell and James are the only players to have won the award four times in five seasons. Moses Malone, Larry Bird and Magic Johnson each won the award three times, while Bob Pettit, Karl Malone, Tim Duncan, Steve Nash, Stephen Curry, Giannis Antetokounmpo and Nikola Jokić have each won it twice. Russell, Chamberlain, and Bird are the only players to win the award in three consecutive years. Only two rookies have won the award: Chamberlain in the 1959–60 season and Wes Unseld in the 1968–69 season. Hakeem Olajuwon of Nigeria, Duncan of the U.S. Virgin Islands, Nash of Canada, Dirk Nowitzki of Germany, Antetokounmpo of Greece, and Jokić of Serbia are the only MVP winners considered "international players" by the NBA.<END_CTX><START_A>Nikola Jokic is the lowest draft pick to win the award. Nikola Jokic was drafted 41st overall by the Denver Nuggets in 2014.<END_A>
What is Methode Traditionalle?		Methode Traditionalle is a method of producing sparkling wine. It was first created in 1531 and involves double fermentation of wine. The second fermentation creates carbon dioxide which creates the bubbles in the sparkling wine.	open_qa	<START_INST>What is Methode Traditionalle?<END_INST><START_CTX><END_CTX><START_A>Methode Traditionalle is a method of producing sparkling wine. It was first created in 1531 and involves double fermentation of wine. The second fermentation creates carbon dioxide which creates the bubbles in the sparkling wine.<END_A>
Which is a bird or fish: Red grouse, Red grouper		Red grouse is a bird, Red grouper is a fish	classification	<START_INST>Which is a bird or fish: Red grouse, Red grouper<END_INST><START_CTX><END_CTX><START_A>Red grouse is a bird, Red grouper is a fish<END_A>
Based on the text below, how would you explain Archimedes’ principle to a 10 years old child, using the example of an object immersed in water?	Any object, totally or partially immersed in a fluid or liquid, is buoyed up by a force equal to the weight of the fluid displaced by the object. Archimedes' principle allows the buoyancy of any floating object partially or fully immersed in a fluid to be calculated. The downward force on the object is simply its weight. The upward, or buoyant, force on the object is that stated by Archimedes' principle above. Thus, the net force on the object is the difference between the magnitudes of the buoyant force and its weight. If this net force is positive, the object rises; if negative, the object sinks; and if zero, the object is neutrally buoyant—that is, it remains in place without either rising or sinking. In simple words, Archimedes' principle states that, when a body is partially or completely immersed in a fluid, it experiences an apparent loss in weight that is equal to the weight of the fluid displaced by the immersed part of the body(s). Consider a cuboid immersed in a fluid, its top and bottom faces orthogonal to the direction of gravity (assumed constant across the cube's stretch). The fluid will exert a normal force on each face, but only the normal forces on top and bottom will contribute to buoyancy. The pressure difference between the bottom and the top face is directly proportional to the height (difference in depth of submersion). Multiplying the pressure difference by the area of a face gives a net force on the cuboid ⁠ ⁠— the buoyancy ⁠ ⁠— equaling in size the weight of the fluid displaced by the cuboid. By summing up sufficiently many arbitrarily small cuboids this reasoning may be extended to irregular shapes, and so, whatever the shape of the submerged body, the buoyant force is equal to the weight of the displaced fluid. weight of displaced fluid = weight of object in vacuum − weight of object in fluid The weight of the displaced fluid is directly proportional to the volume of the displaced fluid (if the surrounding fluid is of uniform density). The weight of the object in the fluid is reduced, because of the force acting on it, which is called upthrust. In simple terms, the principle states that the buoyant force on an object is equal to the weight of the fluid displaced by the object, or the density of the fluid multiplied by the submerged volume times the gravity.	When you immerse an object under water, it pushes the water away in order to take its place. The water wants to get back to where it was before and pushes the object away, towards the surface. Then it depends what is the weight of the object, compared with how much water was moved. Imagine you could put your object on one side of a balance, and on the other side, you would duplicate your object and transform it into water (representing how much water is moved if you were to plunge put your object under water). If your original object is lighter than its equivalent in water, like a bubble for example, or a balloon, the water will be strong enough to have the object to float. If the object is heavier than its equivalent water, like a bowling ball, or a rock, the water will not be able to push hard enough, and the object will sink.	summarization	<START_INST>Based on the text below, how would you explain Archimedes’ principle to a 10 years old child, using the example of an object immersed in water?<END_INST><START_CTX>Any object, totally or partially immersed in a fluid or liquid, is buoyed up by a force equal to the weight of the fluid displaced by the object. Archimedes' principle allows the buoyancy of any floating object partially or fully immersed in a fluid to be calculated. The downward force on the object is simply its weight. The upward, or buoyant, force on the object is that stated by Archimedes' principle above. Thus, the net force on the object is the difference between the magnitudes of the buoyant force and its weight. If this net force is positive, the object rises; if negative, the object sinks; and if zero, the object is neutrally buoyant—that is, it remains in place without either rising or sinking. In simple words, Archimedes' principle states that, when a body is partially or completely immersed in a fluid, it experiences an apparent loss in weight that is equal to the weight of the fluid displaced by the immersed part of the body(s). Consider a cuboid immersed in a fluid, its top and bottom faces orthogonal to the direction of gravity (assumed constant across the cube's stretch). The fluid will exert a normal force on each face, but only the normal forces on top and bottom will contribute to buoyancy. The pressure difference between the bottom and the top face is directly proportional to the height (difference in depth of submersion). Multiplying the pressure difference by the area of a face gives a net force on the cuboid ⁠ ⁠— the buoyancy ⁠ ⁠— equaling in size the weight of the fluid displaced by the cuboid. By summing up sufficiently many arbitrarily small cuboids this reasoning may be extended to irregular shapes, and so, whatever the shape of the submerged body, the buoyant force is equal to the weight of the displaced fluid. weight of displaced fluid = weight of object in vacuum − weight of object in fluid The weight of the displaced fluid is directly proportional to the volume of the displaced fluid (if the surrounding fluid is of uniform density). The weight of the object in the fluid is reduced, because of the force acting on it, which is called upthrust. In simple terms, the principle states that the buoyant force on an object is equal to the weight of the fluid displaced by the object, or the density of the fluid multiplied by the submerged volume times the gravity.<END_CTX><START_A>When you immerse an object under water, it pushes the water away in order to take its place. The water wants to get back to where it was before and pushes the object away, towards the surface. Then it depends what is the weight of the object, compared with how much water was moved. Imagine you could put your object on one side of a balance, and on the other side, you would duplicate your object and transform it into water (representing how much water is moved if you were to plunge put your object under water). If your original object is lighter than its equivalent in water, like a bubble for example, or a balloon, the water will be strong enough to have the object to float. If the object is heavier than its equivalent water, like a bowling ball, or a rock, the water will not be able to push hard enough, and the object will sink.<END_A>
What is Ford		Ford is an American auto manufacturer that is based in Detroit, Michigan. Ford is one of the most popular car brands in the United States and has been since it's inception. It's most popular vehicle is the F150 truck	open_qa	<START_INST>What is Ford<END_INST><START_CTX><END_CTX><START_A>Ford is an American auto manufacturer that is based in Detroit, Michigan. Ford is one of the most popular car brands in the United States and has been since it's inception. It's most popular vehicle is the F150 truck<END_A>
Is plastic bad for us?		Have you ever thought about where your plastic water bottles go? It probably ends up in a landfill, where it will decompose for over 1000 years as it deposits toxic chemicals. Plastic is an invention that has done more harm than good. There are about 363,762,732,605 pounds of plastic covering the earth. Most of this plastic can’t be recycled. Although people think it is too expensive or inconvenient to get rid of plastics, we should reduce the amount of plastic used or increase the price of plastics. One reason is that when plastic invades a balanced ecosystem, it corrupts it completely. The marine biome is one that is affected by this product. The animals in the ocean die of suffocation, ingestion of plastics, and entanglement. The animals affected are seagulls, whales, seals, fish, and turtles, and that's just a few. The animals eat so much plastic that they die of constipation from it. Another reason is the presence of microplastics in food and people. About 99% of fish have microplastics in their stomachs, livers, and filets (the meat of fish). Approximately 80% of cows have microplastics in their meat and milk. A good point is that mealworms and waxworms are studied to digest plastics, and they should be added to landfills to break down plastics. There are about 50,000 particles of plastic in an adult person, and this number is rising fast. Third, most plastic is littered. There are about 10 million tons of plastic litter every year. Every week, you ingest about a credit card's worth of plastic. There are about 44 million pounds of plastic that are produced that is very deadly to the environment."The plastic in the environment is in your water, food, and body. These plastics should not be there; they can lead to cancer, according to a recent study. We need to stop using plastic in our lives. Some ways we can reduce the amount of plastic on earth are by using sustainable materials such as wood, plant fibers, and mushroom styrofoam. We can also try to buy recyclable plastics that have a triangle formed by three cycling arrows. Use a tote bag instead of a plastic one, or a metal and glass straw instead of a plastic one. You can also use a metal cutlery kit instead of plastic. These are all ways you can make a difference in your everyday life.	general_qa	<START_INST>Is plastic bad for us?<END_INST><START_CTX><END_CTX><START_A>Have you ever thought about where your plastic water bottles go? It probably ends up in a landfill, where it will decompose for over 1000 years as it deposits toxic chemicals. Plastic is an invention that has done more harm than good. There are about 363,762,732,605 pounds of plastic covering the earth. Most of this plastic can’t be recycled. Although people think it is too expensive or inconvenient to get rid of plastics, we should reduce the amount of plastic used or increase the price of plastics. One reason is that when plastic invades a balanced ecosystem, it corrupts it completely. The marine biome is one that is affected by this product. The animals in the ocean die of suffocation, ingestion of plastics, and entanglement. The animals affected are seagulls, whales, seals, fish, and turtles, and that's just a few. The animals eat so much plastic that they die of constipation from it. Another reason is the presence of microplastics in food and people. About 99% of fish have microplastics in their stomachs, livers, and filets (the meat of fish). Approximately 80% of cows have microplastics in their meat and milk. A good point is that mealworms and waxworms are studied to digest plastics, and they should be added to landfills to break down plastics. There are about 50,000 particles of plastic in an adult person, and this number is rising fast. Third, most plastic is littered. There are about 10 million tons of plastic litter every year. Every week, you ingest about a credit card's worth of plastic. There are about 44 million pounds of plastic that are produced that is very deadly to the environment."The plastic in the environment is in your water, food, and body. These plastics should not be there; they can lead to cancer, according to a recent study. We need to stop using plastic in our lives. Some ways we can reduce the amount of plastic on earth are by using sustainable materials such as wood, plant fibers, and mushroom styrofoam. We can also try to buy recyclable plastics that have a triangle formed by three cycling arrows. Use a tote bag instead of a plastic one, or a metal and glass straw instead of a plastic one. You can also use a metal cutlery kit instead of plastic. These are all ways you can make a difference in your everyday life.<END_A>
Are rugby and American football similar?		In many regards, yes. In many regards, no. The main similarities are that ball-carrying players are tackled by the opposing team to keep them from scoring the ball in the end zone. The ball is a similar shape, but a different size. The main differences are in the number of players on the field at any given time; 15 players per side in rugby. 11 players per side in football. Also, in football, a player can advance the ball by throwing it forward to a teammate. In rugby, the ball must travel laterally or backward to a teammate by hand. The ball can only be advanced by running or kicking the ball.	open_qa	<START_INST>Are rugby and American football similar?<END_INST><START_CTX><END_CTX><START_A>In many regards, yes. In many regards, no. The main similarities are that ball-carrying players are tackled by the opposing team to keep them from scoring the ball in the end zone. The ball is a similar shape, but a different size. The main differences are in the number of players on the field at any given time; 15 players per side in rugby. 11 players per side in football. Also, in football, a player can advance the ball by throwing it forward to a teammate. In rugby, the ball must travel laterally or backward to a teammate by hand. The ball can only be advanced by running or kicking the ball.<END_A>
What is Bayes theorem	In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to an individual of a known age to be assessed more accurately by conditioning it relative to their age, rather than simply assuming that the individual is typical of the population as a whole. One of the many applications of Bayes' theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in the theorem may have different probability interpretations. With Bayesian probability interpretation, the theorem expresses how a degree of belief, expressed as a probability, should rationally change to account for the availability of related evidence. Bayesian inference is fundamental to Bayesian statistics, being considered by one authority as; "to the theory of probability what Pythagoras's theorem is to geometry."	Bayes theorem is based on conditional probability related to prior knowledge of the event.	information_extraction	<START_INST>What is Bayes theorem<END_INST><START_CTX>In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to an individual of a known age to be assessed more accurately by conditioning it relative to their age, rather than simply assuming that the individual is typical of the population as a whole. One of the many applications of Bayes' theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in the theorem may have different probability interpretations. With Bayesian probability interpretation, the theorem expresses how a degree of belief, expressed as a probability, should rationally change to account for the availability of related evidence. Bayesian inference is fundamental to Bayesian statistics, being considered by one authority as; "to the theory of probability what Pythagoras's theorem is to geometry."<END_CTX><START_A>Bayes theorem is based on conditional probability related to prior knowledge of the event.<END_A>
how to fight a speeding ticket?		Three common ways to resolve a speeding tickets are 1. taking a defensive driving course: it takes about 20 hours to complete the course content but you can speed it up by playing the videos at 2x speed with a chrome plug-in. Some people just let the video play like background noise and do whatever is on the side. 2. hire an attorney to take care of it, the cost depends on your ticket details and varies based on attorneys. But it is the most care-free way as the attorney deals with the court on your behalf. There is no guarantee it always succeeds though. 3. go to the court yourself and fight it. This takes a lot of gut because you will need to face the judge and prosecuting attorney or the traffic policy on the court the defend yourself.	brainstorming	<START_INST>how to fight a speeding ticket?<END_INST><START_CTX><END_CTX><START_A>Three common ways to resolve a speeding tickets are 1. taking a defensive driving course: it takes about 20 hours to complete the course content but you can speed it up by playing the videos at 2x speed with a chrome plug-in. Some people just let the video play like background noise and do whatever is on the side. 2. hire an attorney to take care of it, the cost depends on your ticket details and varies based on attorneys. But it is the most care-free way as the attorney deals with the court on your behalf. There is no guarantee it always succeeds though. 3. go to the court yourself and fight it. This takes a lot of gut because you will need to face the judge and prosecuting attorney or the traffic policy on the court the defend yourself.<END_A>
What is ECharts Java?	ECharts Java is a lightweight but comprehensive library for Java developers to easily use JavaScript visualization library Apache ECharts. The simple chart mode facilitates users to write visualization fast and easily, empowered by the clean APIs provided by ECharts Java. The advanced mode helps create an Option object and its Json representation in chainable Java codes, which includes almost all the features defined in Apache ECharts. Now ECharts Java supports Apache ECharts version 5.x. Features - Simple, clean and organized APIs, supporting method chaining - Full coverage of Apache ECharts functionalities - Easily integrate with Web Frameworks - Flexible export format, including HTML and images - Complete and detailed documentation and examples	ECharts Java is a lightweight, comprehensive library for Java developers to easily integrate JavaScript visualization library Apache ECharts into their web frameworks. The simple and clean APIs facilitate users to develop visualization components fast and easily. For complex and advanced usage, it allows developers to create an Option object and its Json representation. It is compatible with Apache ECharts version 5.x and provides almost full coverage of Apache ECharts functionalities. Here are some highlights of ECharts Java: - Simple, clean, organized APIs with method chaining - Full coverage of Apache ECharts functionalities - Easily integrate with Web Frameworks - Flexible export format, including HTML and images - Complete and detailed documentation and examples	closed_qa	<START_INST>What is ECharts Java?<END_INST><START_CTX>ECharts Java is a lightweight but comprehensive library for Java developers to easily use JavaScript visualization library Apache ECharts. The simple chart mode facilitates users to write visualization fast and easily, empowered by the clean APIs provided by ECharts Java. The advanced mode helps create an Option object and its Json representation in chainable Java codes, which includes almost all the features defined in Apache ECharts. Now ECharts Java supports Apache ECharts version 5.x. Features - Simple, clean and organized APIs, supporting method chaining - Full coverage of Apache ECharts functionalities - Easily integrate with Web Frameworks - Flexible export format, including HTML and images - Complete and detailed documentation and examples<END_CTX><START_A>ECharts Java is a lightweight, comprehensive library for Java developers to easily integrate JavaScript visualization library Apache ECharts into their web frameworks. The simple and clean APIs facilitate users to develop visualization components fast and easily. For complex and advanced usage, it allows developers to create an Option object and its Json representation. It is compatible with Apache ECharts version 5.x and provides almost full coverage of Apache ECharts functionalities. Here are some highlights of ECharts Java: - Simple, clean, organized APIs with method chaining - Full coverage of Apache ECharts functionalities - Easily integrate with Web Frameworks - Flexible export format, including HTML and images - Complete and detailed documentation and examples<END_A>
In which Harry Potter's book did we learn about Voldemort's childhood?		Voldemort's childhood was depicted in Book 6 of Harry Potter and The Half-Bood's Price. Through the private lessons Harry had with Dumbledore, Harry learned about Voldemort's family and upbringing.	open_qa	<START_INST>In which Harry Potter's book did we learn about Voldemort's childhood?<END_INST><START_CTX><END_CTX><START_A>Voldemort's childhood was depicted in Book 6 of Harry Potter and The Half-Bood's Price. Through the private lessons Harry had with Dumbledore, Harry learned about Voldemort's family and upbringing.<END_A>
Give me six story ideas for writing a children's book.		1. A dog gets lost and needs to find its owner. 2. A child is exploring a field and discovers lots of animals hiding in it. 3. A child and their doll go through a day, waking up, eating breakfast, going outside, taking a nap, reading a book, eating dinner, taking a bath, and going to sleep. 4. A giraffe astronaut flies to Mars and discovers friendly Martians who help the giraffe to fix his spaceship and return home to Earth. 5. Ten different animals must work together to pass a letter from a remote village to an apartment in a city. 6. The book can explain the concept of gravity in simple, funny ways which entertain both parents and children.	brainstorming	<START_INST>Give me six story ideas for writing a children's book.<END_INST><START_CTX><END_CTX><START_A>1. A dog gets lost and needs to find its owner. 2. A child is exploring a field and discovers lots of animals hiding in it. 3. A child and their doll go through a day, waking up, eating breakfast, going outside, taking a nap, reading a book, eating dinner, taking a bath, and going to sleep. 4. A giraffe astronaut flies to Mars and discovers friendly Martians who help the giraffe to fix his spaceship and return home to Earth. 5. Ten different animals must work together to pass a letter from a remote village to an apartment in a city. 6. The book can explain the concept of gravity in simple, funny ways which entertain both parents and children.<END_A>
Given these paragraphs about battery storage power stations, what was the levelized cost of electricity from battery storage as of 2020?	As of 2021, the power and capacity of the largest individual battery storage power plants is an order of magnitude less than that of the largest pumped storage power plants, the most common form of grid energy storage. For example, the Bath County Pumped Storage Station, the second largest in the world, can store 24GWh of electricity and dispatch 3GW while the first phase of Vistra Energy's Moss Landing Energy Storage Facility can store 1.2GWh and dispatch 300MW. Grid batteries do not however have to be large, and smaller ones can be deployed widely across a grid for greater redundancy. As of 2019, battery power storage is cheaper than open cycle gas turbine power for use up to two hours, and there was around 365 GWh of battery storage deployed worldwide, growing extremely rapidly. Levelized cost of electricity from battery storage has fallen rapidly, halving in two years to US$150 per MWh as of 2020.	US$150 per MWh.	closed_qa	<START_INST>Given these paragraphs about battery storage power stations, what was the levelized cost of electricity from battery storage as of 2020?<END_INST><START_CTX>As of 2021, the power and capacity of the largest individual battery storage power plants is an order of magnitude less than that of the largest pumped storage power plants, the most common form of grid energy storage. For example, the Bath County Pumped Storage Station, the second largest in the world, can store 24GWh of electricity and dispatch 3GW while the first phase of Vistra Energy's Moss Landing Energy Storage Facility can store 1.2GWh and dispatch 300MW. Grid batteries do not however have to be large, and smaller ones can be deployed widely across a grid for greater redundancy. As of 2019, battery power storage is cheaper than open cycle gas turbine power for use up to two hours, and there was around 365 GWh of battery storage deployed worldwide, growing extremely rapidly. Levelized cost of electricity from battery storage has fallen rapidly, halving in two years to US$150 per MWh as of 2020.<END_CTX><START_A>US$150 per MWh.<END_A>
How many strings does a cello have?	The cello (/ˈtʃɛloʊ/ CHEL-oh), properly violoncello (/ˌvaɪələnˈtʃɛloʊ/ VY-ə-lən-CHEL-oh, Italian pronunciation: [vjolonˈtʃɛllo]), is a bowed (sometimes plucked and occasionally hit) string instrument of the violin family. Its four strings are usually tuned in perfect fifths: from low to high, C2, G2, D3 and A3. The viola's four strings are each an octave higher. Music for the cello is generally written in the bass clef, with tenor clef, and treble clef used for higher-range passages.	A cello has four strings	information_extraction	<START_INST>How many strings does a cello have?<END_INST><START_CTX>The cello (/ˈtʃɛloʊ/ CHEL-oh), properly violoncello (/ˌvaɪələnˈtʃɛloʊ/ VY-ə-lən-CHEL-oh, Italian pronunciation: [vjolonˈtʃɛllo]), is a bowed (sometimes plucked and occasionally hit) string instrument of the violin family. Its four strings are usually tuned in perfect fifths: from low to high, C2, G2, D3 and A3. The viola's four strings are each an octave higher. Music for the cello is generally written in the bass clef, with tenor clef, and treble clef used for higher-range passages.<END_CTX><START_A>A cello has four strings<END_A>
When does daylight savings start and end in Sydney?		Sydney is in New South Wales (NSW), Australia, and NSW participates in daylight savings. Daylight savings starts at 2am on the first Sunday of October; and ends at 3am on the first Sunday in April In 2023: - Daylight savings ends at 3am on Sun 2 April - Daylight savings starts at 2am on Sun 1 October Next year, in 2024: - Daylight savings ends at 3am on Sun 7 April - Daylight savings starts at 2am on Sun 6 October	general_qa	<START_INST>When does daylight savings start and end in Sydney?<END_INST><START_CTX><END_CTX><START_A>Sydney is in New South Wales (NSW), Australia, and NSW participates in daylight savings. Daylight savings starts at 2am on the first Sunday of October; and ends at 3am on the first Sunday in April In 2023: - Daylight savings ends at 3am on Sun 2 April - Daylight savings starts at 2am on Sun 1 October Next year, in 2024: - Daylight savings ends at 3am on Sun 7 April - Daylight savings starts at 2am on Sun 6 October<END_A>
Who is Rihanna?	Robyn Rihanna Fenty (born February 20, 1988) is a Barbadian singer, actress, and businesswoman. Born in Saint Michael and raised in Bridgetown, Barbados, Rihanna auditioned for American record producer Evan Rogers who invited her to the United States to record demo tapes. After signing with Def Jam in 2005, she soon gained recognition with the release of her first two studio albums, Music of the Sun (2005) and A Girl Like Me (2006), both of which were influenced by Caribbean music and peaked within the top ten of the US Billboard 200 chart.	Rihanna (Robyn Rihanna Fenty) is a singer, actress, and businesswoman from Barbados. After auditioning with American record producer Evan Rogers, she signed to Def Jam in 2005 and gained worldwide recognition with her first two studio albums, "Music of the Sun" and "A Girl Like Me".	closed_qa	<START_INST>Who is Rihanna?<END_INST><START_CTX>Robyn Rihanna Fenty (born February 20, 1988) is a Barbadian singer, actress, and businesswoman. Born in Saint Michael and raised in Bridgetown, Barbados, Rihanna auditioned for American record producer Evan Rogers who invited her to the United States to record demo tapes. After signing with Def Jam in 2005, she soon gained recognition with the release of her first two studio albums, Music of the Sun (2005) and A Girl Like Me (2006), both of which were influenced by Caribbean music and peaked within the top ten of the US Billboard 200 chart.<END_CTX><START_A>Rihanna (Robyn Rihanna Fenty) is a singer, actress, and businesswoman from Barbados. After auditioning with American record producer Evan Rogers, she signed to Def Jam in 2005 and gained worldwide recognition with her first two studio albums, "Music of the Sun" and "A Girl Like Me".<END_A>
Tell me whether these are popular sports are more popular in United States of America or in Australia.		Basketball, American Football, Baseball, and Lacrosse are more popular in the United States of America. Soccer, Australian Rules Football, Rugby, and Cricket are more popular in Australia.	classification	<START_INST>Tell me whether these are popular sports are more popular in United States of America or in Australia.<END_INST><START_CTX><END_CTX><START_A>Basketball, American Football, Baseball, and Lacrosse are more popular in the United States of America. Soccer, Australian Rules Football, Rugby, and Cricket are more popular in Australia.<END_A>
Given these paragraphs about Large language models, what is hallucination?	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	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.	closed_qa	<START_INST>Given these paragraphs about Large language models, what is hallucination?<END_INST><START_CTX>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<END_CTX><START_A>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.<END_A>
Does extended screen time cause speech delay in children?	The many other causes of speech delay include bilingual children with phonological disorders, autism spectrum conditions, childhood apraxia, auditory processing disorder, prematurity, cognitive impairment and hearing loss. In addition, when children are addicted to screens, they aren't stimulated to be involved in conversations, causing speech delays.[unreliable source?] Broomfield and Dodd's (2004a)[full citation needed] found out after survey that 6.4% of children who are perfectly normal showed speech difficulty while they lacked these disorders will often show early signs and are at times identified as "at risk" when the speech delay is diagnosed. However, a study done in Saudi Arabia showed no relationship between smart device use and speech delay, although 64.8% of the parents believed that the smart devices are a "problem". A review study from Indonesia points out the existence of contradicting results on that issue.	There is no definite proof that extended screen time cause speech delay in children because there are contradicting study results.	information_extraction	<START_INST>Does extended screen time cause speech delay in children?<END_INST><START_CTX>The many other causes of speech delay include bilingual children with phonological disorders, autism spectrum conditions, childhood apraxia, auditory processing disorder, prematurity, cognitive impairment and hearing loss. In addition, when children are addicted to screens, they aren't stimulated to be involved in conversations, causing speech delays.[unreliable source?] Broomfield and Dodd's (2004a)[full citation needed] found out after survey that 6.4% of children who are perfectly normal showed speech difficulty while they lacked these disorders will often show early signs and are at times identified as "at risk" when the speech delay is diagnosed. However, a study done in Saudi Arabia showed no relationship between smart device use and speech delay, although 64.8% of the parents believed that the smart devices are a "problem". A review study from Indonesia points out the existence of contradicting results on that issue.<END_CTX><START_A>There is no definite proof that extended screen time cause speech delay in children because there are contradicting study results.<END_A>
Do you think depression ran in Wittgenstein's family?	Born in Vienna into one of Europe's richest families, Ludwig Wittgenstein inherited a fortune from his father in 1913. Before World War I, he "made a very generous financial bequest to a group of poets and artists chosen by Ludwig von Ficker, the editor of Der Brenner, from artists in need. These included Trakl as well as Rainer Maria Rilke and the architect Adolf Loos." Later, in a period of severe personal depression after World War I, he gave away his remaining fortune to his brothers and sisters. Three of his four older brothers died by separate acts of suicide. Wittgenstein left academia several times: serving as an officer on the front line during World War I, where he was decorated a number of times for his courage; teaching in schools in remote Austrian villages, where he encountered controversy for using sometimes violent corporal punishment on girls and a boy (the Haidbauer incident) especially during mathematics classes; working during World War II as a hospital porter in London, notably telling patients not to take the drugs they were prescribed; and working as a hospital laboratory technician at the Royal Victoria Infirmary in Newcastle upon Tyne. He later expressed remorse for these incidents, and spent the remainder of his life lecturing and attempting to prepare a second manuscript for publication, which was published posthumously as the hugely influential Philosophical Investigations.	Depression is a mental health issue which can sometimes lead to suicide. It is known to run in families. Ludwig Wittgenstein personally experienced at least one period of severe depression and three of his four older brothers died by separate acts of suicide. Based on the fact that so many of Wittgenstein's immediate family members experienced depression or died by suicide, I think it is fair to say depression ran in Wittgenstein's family.	information_extraction	<START_INST>Do you think depression ran in Wittgenstein's family?<END_INST><START_CTX>Born in Vienna into one of Europe's richest families, Ludwig Wittgenstein inherited a fortune from his father in 1913. Before World War I, he "made a very generous financial bequest to a group of poets and artists chosen by Ludwig von Ficker, the editor of Der Brenner, from artists in need. These included Trakl as well as Rainer Maria Rilke and the architect Adolf Loos." Later, in a period of severe personal depression after World War I, he gave away his remaining fortune to his brothers and sisters. Three of his four older brothers died by separate acts of suicide. Wittgenstein left academia several times: serving as an officer on the front line during World War I, where he was decorated a number of times for his courage; teaching in schools in remote Austrian villages, where he encountered controversy for using sometimes violent corporal punishment on girls and a boy (the Haidbauer incident) especially during mathematics classes; working during World War II as a hospital porter in London, notably telling patients not to take the drugs they were prescribed; and working as a hospital laboratory technician at the Royal Victoria Infirmary in Newcastle upon Tyne. He later expressed remorse for these incidents, and spent the remainder of his life lecturing and attempting to prepare a second manuscript for publication, which was published posthumously as the hugely influential Philosophical Investigations.<END_CTX><START_A>Depression is a mental health issue which can sometimes lead to suicide. It is known to run in families. Ludwig Wittgenstein personally experienced at least one period of severe depression and three of his four older brothers died by separate acts of suicide. Based on the fact that so many of Wittgenstein's immediate family members experienced depression or died by suicide, I think it is fair to say depression ran in Wittgenstein's family.<END_A>
What does an east coast oyster taste like?		East Coast oysters tend to have high salinity and a lot of minerality. Predominant flavors include brine, copper, seaweed, or vegetal tastes. The meat, or texture, is often chewy, distinctly more solid than a West Coast oyster.	open_qa	<START_INST>What does an east coast oyster taste like?<END_INST><START_CTX><END_CTX><START_A>East Coast oysters tend to have high salinity and a lot of minerality. Predominant flavors include brine, copper, seaweed, or vegetal tastes. The meat, or texture, is often chewy, distinctly more solid than a West Coast oyster.<END_A>