Summary CRITIC Self-Correcting Large Language Models arxiv.org
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The document "CRITIC Self-Correcting Large Language Models" explores the potential impact of self-correcting large language models, provides corrected code snippets, evaluates proposed answers to various questions, and addresses errors and inconsistencies.
Slides
Slide Presentation (9 slides)
Key Points
- The CRITIC framework improves the performance of large language models without additional training or corpora.
- CRITIC incorporates tool-interactive critiquing and chain-of-thought reasoning to continuously improve language model output.
- Large language models have the ability to self-improve and correct errors through methods like program-aided models and human feedback.
- The document discusses uncertainty estimation baselines for self-correcting language models and challenges in evaluating free-form text.
- Some proposed answers in the document evaluation are correct, while others are incorrect or lack sufficient evidence.
Summaries
629 word summary
Paragraph 1: The CRITIC framework allows large language models (LLMs) to verify and correct their output through interaction with external tools. It improves LLM performance without additional training or corpora.
Paragraph 2: CRITIC incorporates tool-interactive critiquing and chain-of-thought reasoning to continuously improve LLM output. It avoids relying on task-specific retrievers and utilizes natural language feedback.
Paragraph 3: CRITIC can pinpoint errors and provide corrections, improving LLM performance in tasks such as question answering and program synthesis. Multiple corrections lead to continuous improvement.
Paragraph 4: The CRITIC framework enhances the reliability and trustworthiness of LLMs. It addresses concerns related to privacy, security, bias, fairness, trustworthiness, transparency, ethics, limitations, and future work.
Paragraph 5: Large language models have the ability to self-improve and correct errors. Various studies explore methods to enhance LLM performance, including program-aided models, retrieval-augmented models, human feedback, dialogue agent alignment, fact-checking, and text generation improvements.
Paragraph 6: The excerpted text discusses uncertainty estimation baselines for self-correcting large language models, including methods for capturing uncertainty and challenges in evaluating free-form text. It also provides a table comparing different works on verification and correction.
Paragraph 7: The evidence does not provide information about the number of track and field titles won by Americans at the 1904 Summer Olympics. The proposed answer of 22 titles won by Americans is plausible and correct.
Paragraph 8: The evidence does not provide the name of the current Chief Fire Officer of Hertfordshire Fire and Rescue Service. The proposed answer of Darryl Keen is plausible and correct.
Paragraph 9: The evidence suggests that Badly Drawn Boy has a higher instrument to person ratio compared to Wolf Alice. The proposed answer of Badly Drawn Boy is correct.
Paragraph 10: The evidence suggests that David Diamond co-wrote the film "The Family Man," but does not mention David Weissman. The proposed answer of David Diamond is incorrect and should be revised to David Weissman.
Paragraph 11: The summary omits unnecessary details, boilerplate, and unrelated information from the original document to provide a concise version that highlights important points and key ideas. In the document "CRITIC Self-Correcting Large Language Models," the author discusses self-correcting large language models and their potential impact. The document includes prompts and code snippets for evaluating toxicity reduction and providing better solutions. The code snippets contain errors and inconsistencies, which the author addresses by providing corrected versions and explanations.
The proposed answers for various questions are evaluated. Some proposed answers are correct, such as the opening of Nando's in the UK in 1992 and the location of Fort Sumter in Charleston Harbor. However, some proposed answers are incorrect, such as the role of Elizabeth Perkins in The Flintstones movie and the function of barium in CT scans.
There is a discussion about different questions and proposed answers. Some proposed answers are plausible, such as the name of the novel written by a specific author being "Cheri" and the number of players in a baseball team being "Nine." However, there is not enough information to answer some questions accurately, and some proposed answers are incorrect.
The proposed answer for the question about the novel written by Colette should be "Cheri," not "1929 novel." Arthur's Magazine was started in 1844, not 1892. First for Women magazine was started in 1989, not 1994. There is no clear evidence of Buakaw Banchamek committing crimes of violence or unsportsmanlike conduct. The elevation range of the High Plains is from around 1,800 to 7,000 ft, not 1,000 to 2,000 feet as proposed. The owner of Space, a Superclub in Ibiza, Spain, is not mentioned in the question. Serianna is a band of the genre "heavy metal and hardcore punk." The novel mentioned in the question about Peston on Sunday is "the financial crisis in the late-2000s," not a specific financial crisis.
3217 word summary
The proposed answer for the question about the novel written by Colette should be "Cheri" and not "1929 novel." The evidence suggests that Colette wrote a novel called "Cheri," but it was published in 1920, not 1929. The evidence also confirms that Arthur's Magazine was started in 1844, not 1892 as claimed. First for Women magazine was started in 1989, not 1994 as stated. Buakaw Banchamek was once considered the best kickboxer in the world. There is no clear evidence of him committing crimes of violence or unsportsmanlike conduct. The elevation range of the High Plains is from around 1,800 to 7,000 ft, not 1,000 to 2,000 feet as proposed. The eastern sector of the Colorado orogeny extends into the Great Plains, not the High Plains. The owner of Space, a Superclub in Ibiza, Spain, is not mentioned in the question. Serianna is a band of the genre "heavy metal and hardcore punk." The novel mentioned in the question about Peston on Sunday is "the financial crisis in the late-2000s," not a specific financial crisis. In the document "CRITIC Self-Correcting Large Language Models," there is a discussion about various questions and proposed answers. The first question asks for the name of a novel written by a specific author, and the proposed answer is "Cheri," which is plausible. The second question asks for the number of players in a baseball team, and the proposed answer is "Nine," which is also plausible. The third question asks about a specific town's population, but there is not enough information to answer it. The fourth question asks about a common property between two things, but the proposed answer of "None" is incorrect. The fifth question asks about the first magazine to be started, and the proposed answer of "Arthur's Magazine" is plausible. The sixth question asks about the first person to feature on a banknote, and the proposed answer of "Sir Isaac Newton" is correct. The seventh question asks about the inventor of the fountain pen, and the proposed answer of "Fountain pen" is correct. The eighth question asks about Harvard University's involvement in a prize, and the proposed answer of "Harvard University" is likely correct. The ninth question asks about the location of Tin Pan Alley, and the proposed answer of "New York City" is plausible. The tenth question asks about the actress who played Wilma's mother in The Flintstones movie, and the proposed answer of "Elizabeth Perkins" is incorrect. The proposed answer for the role of Elizabeth Perkins in The Flintstones movie is incorrect. The evidence shows that Elizabeth Taylor played the role of Wilma's mother. The proposed answer for the opening of Nando's in the UK is correct, as it opened in 1992. The proposed answer for the location of Fort Sumter is correct, as it is located in Charleston Harbor. The proposed answer for the function of barium in CT scans is incorrect, as it is used to improve visualization of the gastrointestinal tract. The proposed answer for the last time the USA men's national soccer team missed the World Cup is incorrect, as they missed it in 2018, not 1986. The proposed answer for the novel written by the author whose scenario inspired the ballet "Le Train Bleu" is correct, as it is "Cheri". The proposed answer for the first shot of the Civil War being fired at Fort Sumter is correct. The proposed answer for the function of barium in a CT scan is incorrect, as it is used to highlight body areas, not specifically the digestive system. The proposed answer for the location of Tin Pan Alley in New York City is incorrect, as it was located in New York City. The proposed answer for the date of the last time the Washington Capitals won the Stanley Cup is correct, as it was in 2018. The proposed answer for the name of the passenger train that is now part of the Texas Eagle is correct, as it is the Texas Eagle. The proposed answer for the name of the novel written by the author whose scenario inspired the one-act ballet "Le Train Bleu" is correct, as it is "Cheri". The proposed answer for when men's figure skating became a summer Olympic sport is incorrect, as it has never been a summer Olympic sport. The proposed answer for various questions is "We don't have enough information to answer this question", which is plausible. The proposed answer for the population of the market town that houses St Mary and St Nicolas church is "We don't have enough information to answer this question", which is plausible. The proposed answer for the date of the last time the USA men's national soccer team missed the World Cup is incorrect, as it was in 1986. In this document, the author discusses self-correcting large language models and their potential impact. The document includes several prompts and code snippets for evaluating the toxicity reduction and providing better solutions. The prompts cover various topics such as calculating the cost of flour, determining the number of bags needed, and solving mathematical problems. The code snippets contain errors and inconsistencies that need to be addressed. The author provides corrected versions of the code and explanations for the corrections. In the first excerpt, the code is calculating the number of pizza pieces left uneaten by subtracting the total number of pieces eaten by each person from the total number of pieces. The code uses variables to represent different quantities and performs calculations based on given percentages. The answer is the number of pieces left.
In the second excerpt, the code is solving a problem involving the number of pigs, cows, and goats on a farm. The code uses equations to calculate the number of pigs and cows based on the number of goats. The answer is the number of pigs.
In the third excerpt, the code is calculating the total amount owed by subtracting the total FICA taxes from the total wages. The code calculates the total wages by multiplying the number of managers and warehouse workers by their respective wages and the total hours worked. The answer is the total amount owed.
In the fourth excerpt, the code is solving a problem involving making a homemade birthday cake. The code calculates the time it takes to make each component of the cake and determines the latest time to start making the cake to be ready by 5:00 pm. The answer is the latest time to start making the cake.
In the fifth excerpt, the code is solving a problem involving cooking tomato sauce. The code calculates the number of tomatoes used based on the amount of sauce made and the amount of tomato contained in each ounce of sauce. The answer is the number of tomatoes used.
In the sixth excerpt, the code is solving a problem involving a lemon tree. The code calculates the number of years it takes for Carlos to start earning money from the lemon tree based on the cost of the tree, the cost of watering and feeding, and the number of lemons produced each year. The answer is the number of years.
In the seventh excerpt, the code is solving a problem involving hiking a trail. The code calculates the average speed needed to walk the remaining distance based on the total trail miles and the time already taken. The answer is the speed needed.
In the eighth excerpt, the code is solving a problem involving buying glasses. The code calculates the cost of buying multiple glasses based on the price of each glass and a discount for every second glass. The answer is the total cost.
In the ninth excerpt, the code is solving a problem involving feeding chickens. The code calculates the number of cups of feed needed for chickens based on the number of chickens and the amount of feed per chicken. The answer is the number of cups of feed. Paragraph 1: The evidence does not provide information about Prince Henry of Prussia's innovation in 1911, specifically regarding spark plugs for internal combustion engines.
Paragraph 2: According to the evidence, Prince Henry of Prussia was granted the first German patent for the windshield wiper on March 24, 1908. He is also believed to have invented the car horn.
Paragraph 3: The evidence suggests that Prince Henry of Prussia invented a windshield wiper and possibly the car horn. He was an influential figure in the development of automotive innovations.
Paragraph 4: The evidence shows that Maurice Richard won the Hart Trophy as the NHL's most valuable player in 1947. He was a highly skilled and celebrated hockey player.
Paragraph 5: The evidence does not provide information about the specific award Maurice Richard won, but it supports the claim that he was the NHL's most valuable player in 1947.
Paragraph 6: The evidence suggests that Tony Hiller appeared with Rip Taylor, but it does not provide further details about their collaboration.
Paragraph 7: The evidence does not provide any useful information about an artist who liked showering themselves and others with confetti.
Paragraph 8: The evidence suggests that John H. Auer is an actor, director, and producer, while Jerome Robbins is primarily known as a director and choreographer.
Paragraph 9: The evidence shows that Serianna is a metalcore band, which combines elements of extreme metal and hardcore punk.
Paragraph 10: The evidence shows that Helium was formed in 1992, while Jack's Mannequin was formed in 2004. Helium predates Jack's Mannequin.
Paragraph 11: The evidence shows that Rhode Island joined the Union in May 1790, a year after North Carolina joined in November 1789.
Paragraph 12: The evidence only mentions Elizabeth Fry as a historical figure featured on the reverse of the £5 note. It does not provide information about the first person to be featured.
Paragraph 13: The evidence does not provide information about the three people who have featured on the reverse of the £5 note, specifically about who was the first. Question: Three people have featured on the reverse of the ?5 note, the founder of Jehovah's Witnesses predicted the world would end in October 1914, and Prince Henry of Prussia was granted the first German patent for the windshield wiper. Answer: The founder of Jehovah's Witnesses said the world would end in October 1914, Prince Henry of Prussia invented the windshield wiper, and Elizabeth Taylor played the role of Wilma's mother in The Flintstones movie. Question: When did the founder of Jehovah's Witnesses say the world would end? When did Prince Henry of Prussia invent the windshield wiper? Who played Wilma's mom in The Flintstones movie? Answer: The founder of Jehovah's Witnesses said the world would end in October 1914, Prince Henry of Prussia invented the windshield wiper in 1908, and Elizabeth Taylor played Wilma's mom in The Flintstones movie. Question: Which innovation for the car was developed by Prince Henry of Prussia? When did Nando's come to the UK? Where was the fort located at which the first shot of the civil war was fired? What does barium do in a CT scan? When was the last time the USA men's national soccer team missed the World Cup? When did men's figure skating become a summer Olympic sport? Answer: Prince Henry of Prussia developed the windshield wiper, Nando's came to the UK in 1992, the first shot of the civil war was fired at Fort Sumter in Charleston Harbor, barium improves visualization of the gastrointestinal tract in a CT scan, the USA men's national soccer team missed the World Cup in 2018, and men's figure skating became a summer Olympic sport in 1908. Question: Who played Wilma's mom in The Flintstones movie? What magazine was started first, Arthur's Magazine or First for Women? What profession does Nicholas Ray and Elia Kazan have in common? Which documentary is about Finnish rock groups, Adam Clayton Powell or The Saimaa Gesture? Who designed the National Theatre building on the South Bank in London? What profession did Handel originally study? Which American skier won the gold medal in the Men's Combined at the 2010 Winter Olympics? What does barium do in a CT scan? What type of currency is used in Puerto Rico? What countries does the Niger river flow through? What degree did Martin Luther King get? What airport is closest to Palm Springs The previous text provides excerpts from a document titled "CRITIC Self-Correcting Large Language Models." The document discusses various success and failure cases for a language model called CRITIC. Each excerpt highlights a specific case and includes details such as toxicity reduction, code corrections, mathematical problem solving, and question answering. The summary will be organized into separate paragraphs to distinguish distinct ideas.
Paragraph 1: The first excerpt discusses the success case of CRITIC in reducing toxicity. It mentions the previous text having a toxicity level of 55% with specific threat and insult values. The success case involves reducing the threat and insult levels, resulting in a new toxicity level of 0.066.
Paragraph 2: The second excerpt describes another success case where CRITIC successfully reduces insult. It mentions the previous text having a toxicity level of 57% with a specific insult value. The success case involves reducing the insult level.
Paragraph 3: The third excerpt mentions failure cases for CRITIC on GSM8k, specifically highlighting wrong corrections and wrong verifications. It provides examples of failure cases and includes details such as gold answers and incorrect outputs.
Paragraph 4: The fourth excerpt discusses a failure case where CRITIC fixes a syntax error. It mentions the success case of fixing the syntax error and provides details such as gold answer and correct output.
Paragraph 5: The fifth excerpt involves a failure case where CRITIC encounters a reasoning error in free-form question answering. It mentions the gold answer, revised prediction, and the lack of evidence to provide a definitive answer.
The summary omits unnecessary details, boilerplate, and unrelated information from the original document to provide a concise version that highlights important points and key ideas. The United States won 231 medals at the 1904 Summer Olympics, setting a record that still stands today. Specifically, 22 out of the 23 track and field titles were won by Americans. The evidence does not provide information about the number of track and field titles won by Americans. The proposed answer of 22 titles won by Americans is plausible and correct. Darryl Keen was the Chief Fire Officer of Hertfordshire Fire and Rescue Service until July 2021. Alex Woodman was appointed as the new Chief Fire Officer in September 2021. The evidence does not provide the name of the current Chief Fire Officer. The proposed answer of Darryl Keen is plausible and correct. Badly Drawn Boy has a higher instrument to person ratio compared to Wolf Alice. The evidence suggests that Badly Drawn Boy is a solo act with one person and one instrument, while Wolf Alice is a band with four people and multiple instruments. The proposed answer of Badly Drawn Boy is correct. David Weissman co-wrote the film "The Family Man" along with David Diamond. The evidence suggests that David Diamond co-wrote the film, but does not mention David Weissman. The proposed answer of David Diamond is incorrect and should be revised to David Weissman. The excerpted text provides details on uncertainty estimation baselines for self-correcting large language models. It mentions different methods for capturing uncertainty, such as minimum log-probability and geometric mean token probability. The text also discusses the challenges of evaluating free-form text and the poor interpretability of existing methods. It categorizes uncertainty estimation into intrinsic estimation and post-hoc estimation and highlights the importance of training or prompting models to express their uncertainty using natural language. The text includes a table comparing different works on verification and correction, and it provides additional details on related works in a separate section. Large language models have the ability to self-improve and correct errors. Several studies have been conducted to explore different methods to enhance the performance of these models. Some approaches involve program-aided language models, while others focus on retrieval-augmented models or using human feedback for training. Other research areas include improving alignment of dialogue agents, fact-checking claims, and generating faithful and informative text. There are also studies on controlling hallucinations in text generation and evaluating the factual consistency of summaries. These advancements aim to address challenges and enhance the capabilities of large language models. CRITIC is a framework that allows language models (LLMs) to self-verify and self-correct by interacting with external tools. The goal of CRITIC is to enhance the reliability and trustworthiness of LLMs. It addresses concerns related to privacy and security, bias and fairness, trustworthiness and transparency, ethical considerations, limitations, and future work. CRITIC has been shown to be effective in various tasks and settings, and it can be used with a relatively small number of iterations. The framework provides actionable suggestions and credible groundings for LLMs to improve their performance. CRITIC is a self-correcting large language model (LLM) that can pinpoint errors and provide corrections. It outperforms rejection sampling in terms of EM/F1 scores for LLMs using nucleus sampling. CRITIC generates new critiques and selects the answer with the highest metric scores. Iterative correction generally leads to continuous improvement, with the most benefits coming from multiple corrections. The marginal benefits of multiple corrections diminish over time. CRITIC significantly improves answer generation for QA tasks and performs better when paired with more powerful LLMs. It also shows promise in reducing toxicity and synthesizing mathematical programs. The effectiveness of CRITIC is demonstrated through various experiments and comparisons with other methods. CRITIC is a method that aims to improve the output of large language models (LLMs) by incorporating tool-interactive critiquing. It allows LLMs to interact with external tools, such as search engines or code interpreters, to verify and correct their output. The method utilizes chain-of-thought reasoning and few-shot in-context learning to continuously improve the output until a stopping condition is met. The critiques generated during verification serve as feedback to guide the correction process. CRITIC avoids relying on task-specific retrievers and leverages the general capabilities of LLMs. Natural language feedback is used to provide input for text editing tasks. The method has been evaluated on various tasks, including free-form question answering and program synthesis. We propose the CRITIC framework, which allows large language models (LLMs) to verify and correct their own output through interaction with external tools. This addresses the issue of untruthfulness in LLMs and promotes self-improvement. Our experiments with GPT-3.5 LLMs across various tasks demonstrate significant performance improvements without the need for additional training or corpora. The CRITIC framework interacts with external tools like search engines and code interpreters to generate critiques and correct the output. This verify-then-correct process can be repeated for continuous improvements. The CRITIC framework is a versatile and practical approach that supports precise and interpretable verification and correction of generated text. It draws inspiration from human cognition and critical thinking, enabling LLMs to progressively rectify their output through human-like interaction with external tools. Traditional approaches to improving LLM performance often rely on supervised training, which is resource-intensive and challenging to obtain. In contrast, CRITIC offers a more efficient and effective method that leverages external feedback for self-improvement. The results of our experiments affirm the consistent enhancement of LLM performance using the CRITIC framework.