arxiv: 2305.11738 · v4 · submitted 2023-05-19 · 💻 cs.CL · cs.AI

Recognition: 3 theorem links

· Lean Theorem

CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

Zhibin Gou , Zhihong Shao , Yeyun Gong , Yelong Shen , Yujiu Yang , Nan Duan , Weizhu Chen

Authors on Pith no claims yet

Pith reviewed 2026-05-13 18:55 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords large language modelsself-correctiontool interactioncritiquingquestion answeringprogram synthesistoxicity reductionexternal feedback

0 comments

The pith

Large language models can self-correct outputs by using external tools to critique and revise them.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents CRITIC, a framework that lets LLMs begin with an initial response and then call tools such as search engines or code interpreters to check facts, debug code, or flag toxic content. The model collects this feedback and produces a revised version, repeating the process as needed. Experiments on free-form question answering, mathematical program synthesis, and toxicity reduction show consistent gains over baselines that lack tool interaction. The central point is that external feedback enables LLMs to improve their own outputs in ways that mirror how humans cross-check work. This approach targets common failures like hallucinations and incorrect generations without requiring model retraining.

Core claim

CRITIC allows LLMs to validate and progressively amend their own outputs by interacting with appropriate tools to evaluate certain aspects of the text and then revising the output based on the feedback obtained during this validation process, with comprehensive evaluations showing consistent performance enhancements in free-form question answering, mathematical program synthesis, and toxicity reduction.

What carries the argument

CRITIC, the framework in which an LLM starts with an initial output, queries external tools for targeted feedback on facts or quality, and revises the output using that feedback.

If this is right

Question-answering accuracy rises when models cross-check facts against search results before finalizing answers.
Mathematical program synthesis produces fewer errors after models debug candidate code with an interpreter.
Generated text shows lower toxicity rates once models receive explicit feedback from toxicity classifiers.
Ongoing self-improvement in LLMs depends on access to external validation signals rather than internal knowledge alone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Future systems might embed tool queries as a default step instead of an optional add-on for reliability.
The same loop could extend to other generation tasks such as dialogue or summarization if suitable feedback tools exist.
Reducing reliance on ever-larger models becomes possible if iterative tool-based correction already delivers gains.

Load-bearing premise

External tools return accurate, relevant feedback that the LLM can reliably interpret and turn into a measurably better revision.

What would settle it

Running CRITIC on the same evaluation tasks and observing no gains or outright declines in accuracy, correctness, or toxicity scores would show the self-correction loop does not work as claimed.

read the original abstract

Recent developments in large language models (LLMs) have been impressive. However, these models sometimes show inconsistencies and problematic behavior, such as hallucinating facts, generating flawed code, or creating offensive and toxic content. Unlike these models, humans typically utilize external tools to cross-check and refine their initial content, like using a search engine for fact-checking, or a code interpreter for debugging. Inspired by this observation, we introduce a framework called CRITIC that allows LLMs, which are essentially "black boxes" to validate and progressively amend their own outputs in a manner similar to human interaction with tools. More specifically, starting with an initial output, CRITIC interacts with appropriate tools to evaluate certain aspects of the text, and then revises the output based on the feedback obtained during this validation process. Comprehensive evaluations involving free-form question answering, mathematical program synthesis, and toxicity reduction demonstrate that CRITIC consistently enhances the performance of LLMs. Meanwhile, our research highlights the crucial importance of external feedback in promoting the ongoing self-improvement of LLMs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

CRITIC gives LLMs a tool-based critique-and-revise loop that reports gains on QA, code, and toxicity, but the gains could come from extra revision steps rather than the feedback itself.

read the letter

The main takeaway is that this paper describes a loop where an LLM starts with an answer, calls external tools to check it, and then revises based on the tool output. They test it on free-form QA, math program synthesis, and toxicity reduction and say it improves results across the board. The framework itself is the concrete new piece: it turns tool use into an explicit validation-plus-revision cycle rather than just retrieval or one-shot prompting. That matches how people actually work and gives a clear way to handle hallucination and bad code without retraining. The motivation is straightforward and the three tasks cover different failure modes, so the idea has practical reach for anyone already wiring LLMs to search or interpreters. The abstract claims consistent gains, but it gives no numbers, no error bars, and no mention of a matched baseline that does the same number of LLM calls without tools. If the experiments skip that control, the improvement could just be from having another generation pass. The stress-test concern lands until the full paper shows otherwise. The work is empirical with external benchmarks, so no circularity issues there. This is worth a reading group to walk through the exact prompts and tool calls. I would not cite it yet, but the core pattern is clear enough that a serious editor should send it to referees to check the controls and the size of the effect.

Referee Report

2 major / 2 minor

Summary. The paper introduces the CRITIC framework, in which LLMs generate an initial output, interact with external tools (e.g., search engines, code interpreters) to obtain critiques on specific aspects such as factual accuracy or code correctness, and then revise the output based on that feedback. Evaluations are reported on free-form question answering, mathematical program synthesis, and toxicity reduction, with the claim that CRITIC produces consistent performance gains over base LLMs and highlights the value of external feedback for ongoing self-improvement.

Significance. If the central claim is supported after proper controls, the work would be significant for showing a practical mechanism by which LLMs can leverage real-world tools to reduce hallucinations and improve output quality, moving beyond purely internal generation or prompting techniques.

major comments (2)

[Section 4] Section 4 (Experiments): the reported baselines do not include a matched self-revision condition that performs the same number of LLM calls and revision steps without any tool feedback. This control is required to isolate whether gains arise from the tool-interactive critiquing component or simply from additional generation passes, directly affecting attribution of the central claim.
[Section 4.2] Section 4.2 (Task-specific results): quantitative tables and error analysis are needed to substantiate the abstract's claim of 'consistent' gains; without reported effect sizes, statistical significance, or breakdown by error type, it is difficult to assess whether improvements are robust or task-specific.

minor comments (2)

[Abstract] Abstract: specific numerical improvements, baseline names, and dataset sizes should be added so readers can immediately gauge the magnitude of the reported gains.
[Figure 2] Figure 2 and Algorithm 1: the flow diagram and pseudocode would benefit from explicit annotation of the exact prompt templates used for tool calls and revision steps to improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree with the need for stronger controls and analyses to support our claims and will update the manuscript accordingly.

read point-by-point responses

Referee: [Section 4] Section 4 (Experiments): the reported baselines do not include a matched self-revision condition that performs the same number of LLM calls and revision steps without any tool feedback. This control is required to isolate whether gains arise from the tool-interactive critiquing component or simply from additional generation passes, directly affecting attribution of the central claim.

Authors: We agree that this control is necessary to properly attribute the improvements to the tool-interactive component. In the revised version, we will include a matched self-revision baseline that performs the same number of LLM calls and revision steps but without tool feedback. This will allow us to isolate the effect of the external critiques. revision: yes
Referee: [Section 4.2] Section 4.2 (Task-specific results): quantitative tables and error analysis are needed to substantiate the abstract's claim of 'consistent' gains; without reported effect sizes, statistical significance, or breakdown by error type, it is difficult to assess whether improvements are robust or task-specific.

Authors: We acknowledge the need for more rigorous quantitative reporting. We will add tables with effect sizes, statistical significance tests, and error analysis broken down by error types in the revised Section 4.2 to substantiate the consistency of the gains across tasks. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical framework

full rationale

The paper introduces an empirical framework (CRITIC) for LLM self-correction via tool interaction and evaluates it on external benchmarks for QA, code synthesis, and toxicity reduction. No mathematical derivations, fitted parameters, or predictions are claimed; performance gains are measured directly against baselines on held-out data. The central claim rests on experimental outcomes rather than any self-referential equations or load-bearing self-citations that reduce to the inputs by construction. This is a standard empirical result with no circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that LLMs can translate tool feedback into improved generations; no free parameters or invented physical entities are introduced.

axioms (1)

domain assumption LLMs can effectively revise outputs when given structured external feedback from tools
This is the load-bearing premise that allows the self-correction loop to succeed.

invented entities (1)

CRITIC framework no independent evidence
purpose: A structured process for tool-interactive self-critiquing and revision
New method introduced by the paper; no independent evidence outside the described experiments.

pith-pipeline@v0.9.0 · 5506 in / 1138 out tokens · 36314 ms · 2026-05-13T18:55:38.362369+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

LedgerForcing conservation_from_balance unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

our research highlights the crucial importance of external feedback in promoting the ongoing self-improvement of LLMs
HierarchyEmergence hierarchy_emergence_forces_phi unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

CRITIC consistently enhances the performance of LLMs

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

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Reference graph

Works this paper leans on

42 extracted references · 42 canonical work pages · cited by 24 Pith papers

[1]

doi: 10.18653/v1/2020.findings-emnlp.301

Association for Computational Linguistics. doi: 10.18653/v1/2020.findings-emnlp.301. URL https://aclanthology.org/2020.findings-emnlp.301. Taisiya Glushkova, Chrysoula Zerva, Ricardo Rei, and André F. T. Martins. Uncertainty-aware machine translation evaluation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 3920–3938, Punta...

work page doi:10.18653/v1/2020.findings-emnlp.301 2020
[2]

Ximing Lu, Sean Welleck, Liwei Jiang, Jack Hessel, Lianhui Qin, Peter West, Prithviraj Am- manabrolu, and Yejin Choi

URL https://openreview.net/forum?id=DHyHRBwJUTN. Ximing Lu, Sean Welleck, Liwei Jiang, Jack Hessel, Lianhui Qin, Peter West, Prithviraj Am- manabrolu, and Yejin Choi. Quark: Controllable text generation with reinforced unlearning. CoRR, abs/2205.13636, 2022. doi: 10.48550/arXiv.2205.13636. URL https://doi.org/10. 48550/arXiv.2205.13636. Aman Madaan, Niket...

work page doi:10.48550/arxiv.2205.13636 2022
[3]

URL https://www.microsoft.com/en-us/research/publication/ capabilities-of-gpt-4-on-medical-challenge-problems/ . OpenAI. Gpt-4 technical report, 2023. Myle Ott, Michael Auli, David Grangier, and Marc’Aurelio Ranzato. Analyzing uncertainty in neural machine translation. In International Conference on Machine Learning, pp. 3956–3965. PMLR, 2018. Long Ouyang...

work page doi:10.18653/v1/2021.naacl-main 2023
[4]

Check your facts and try again: Improving large language models with external knowledge and automated feedback

URL https://aclanthology.org/2021.naacl-main.168. Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, et al. Check your facts and try again: Improving large language models with external knowledge and automated feedback. arXiv preprint arXiv:2302.12813, 2023. Ofir Press, Muru Zhang, Sewon...

work page arXiv 2021
[5]

Self-Verification and Self-Correction

Association for Computational Linguistics. URL https://aclanthology.org/ 2022.emnlp-main.296. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural L...

work page arXiv 2022
[6]

David Diamond

Plausibility: The question asks for a name, and the answer "David Diamond" is a name. So it’s plausible

work page
[7]

Evolution

Truthfulness: Let’s search the question in google: > Search Query: What screenwriter with credits for "Evolution" co-wrote a film starring Nicolas Cage and Tea Leoni? > Evidence: [David Weissman - Wikipedia] Evolution was based on a story by Don Jakoby, who turned it into a screenplay along with Diamond and Weissman... The evidence suggests that David Wei...

work page 2024
[8]

Wolf Alice

Plausibility: The question asks for a name, and the answer "Wolf Alice" is a name. So it’s plausible

work page
[9]

The evidence suggests that Wolf Alice is a four-piece alternative rock band

Truthfulness: Let’s search the question in google: > Search Query: Which performance act has a higher instrument to person ratio, Badly Drawn Boy or Wolf Alice? > Evidence: [nlpproject2023/Sentences - Datasets at Hugging Face] ...Wolf Alice are a four-piece alternative rock band from... The evidence suggests that Wolf Alice is a four-piece alternative roc...

work page 1995
[10]

Darryl Keen

Plausibility: The question asks for the name of the chief fire officer of Hertfordshire fire and rescue service, and the answer is "Darryl Keen", which is a name. So it’s plausible

work page
[11]

The evidence does not provide the name of the chief fire officer

Truthfulness: Let’s search the question in google: > Search Query: Who is the Hertfordshire fire and rescue service chief fire officer? site: hertfordshire.gov.uk > Evidence: [Hertfordshire Fire and Rescue Service - Wikipedia] Hertfordshire Fire and Rescue Service (HFRS) is ... The evidence does not provide the name of the chief fire officer. Let’s search...

work page 2021
[12]

So the answer is: Alex Woodman (since September 2021), Darryl Keen (until July 2021). Revised answer: Alex Woodman (since September 2021), Darryl Keen (until July 2021) Gold answer: [’Darryl Keen’] (outdated) EM/F1: False/0.33 E.1.2 F AILURE CASES Listing 4: Failure case for CRITIC on free-form question answering: Not enough evidence. Question: In the 190...

work page 2021
[13]

So it’s plausible

Plausibility: The question asks for a number, and the answer is "22", which is a number. So it’s plausible

work page
[14]

located at what is now known as Francis Field on the campus of Washington University in St

Truthfulness: Let’s search the question in google: > Search Query: In the 1904 Olympics, how many of the 23 track and field titles were won by Americans? site: wikipedia.org > Evidence: [1904 Summer Olympics - Wikipedia] The 1904 Summer Olympics were an international multi-sport ... located at what is now known as Francis Field on the campus of Washington...

work page 1904
[15]

Birmingham

Plausibility: The question asks for a city, and the answer "Birmingham" is a city. So it’s plausible

work page
[16]

where is the headquarters of the SEC?

Truthfulness: 35 Published as a conference paper at ICLR 2024 Let’s search the question in google: > Search Query: The Tennessee Volunteers football team plays as a member for a conference in what city? site: wikipedia.org > Evidence: [Tennessee Volunteers - Wikipedia] The Tennessee Volunteers and Lady Volunteers ... represent the University of Tennessee ...

work page 2024
[17]

NameError

The above code causes the "NameError" because it use the variable ‘ num_of_fruits_in_10th_year‘ before it is defined

work page
[18]

unsupported operand type(s) for +: ’int’ and ’str’

The order of the calculation is not correct, ‘num_of_fruits_in_8th_year‘ should be calculated after ‘ num_of_fruits_in_10th_year‘. Let’s analysis the problem, we can calculate the number of fruits for each year based on the description in the question. Here’s a better solution: ‘‘‘python initial_fruits = 50 num_of_fruits_in_6th_year = 3 * initial_fruits n...

work page
[19]

TypeError

The code causes a "TypeError" because the variable ‘x‘ is not defined. 37 Published as a conference paper at ICLR 2024

work page 2024
[20]

We should set the cost of the two venues to be equal to each other, and then solve for the number of guests required

The equation in the code is not correct. We should set the cost of the two venues to be equal to each other, and then solve for the number of guests required. Here’s a better solution: ‘‘‘python flat_fee = 200 cost_per_person = 25 cost_of_food_per_person = 5 num_of_guests = flat_fee / (cost_per_person + cost_of_food_per_person) answer = num_of_guests ‘‘‘ ...

work page
[21]

The number of guests should be an integer, 6.66667 is not reasonable

work page
[22]

> flat_fee = 200 > # While the second charges, $25 per person who attends

Let’s check the code: > # The first venue charges a flat fee of $200, regardless of how many guests attend. > flat_fee = 200 > # While the second charges, $25 per person who attends. > cost_per_person = 25 > # However, the first venue does not include food, which Mark estimates will cost $5 for each person who attends. > cost_of_food_per_person = 5 > num_...

work page 2024
[23]

The percentage of students enrolled in hip-hop dance should be a positive number between 0 and100, 55 > 0 and 55 < 100, it’s reasonable

work page
[24]

> answer = percent_of_hip_hop_dance This assigns the answer to the variable ‘answer‘, it’s correct

Let’s check the code: > num_of_students = 20 > percent_of_contemporary_dance = 20 > percent_of_jazz_dance = 25 > percent_of_hip_hop_dance = 100 - percent_of_contemporary_dance - percent_of_jazz_dance This calculates the percentage of students enrolled in hip-hop dance, it’ s correct. > answer = percent_of_hip_hop_dance This assigns the answer to the varia...

work page 2024
[25]

TimeoutError

The code causes the "TimeoutError" because it runs in an infinite loop

work page
[26]

following him

Let’s check the code: > num_of_snowballs = 60 > snowballs_per_hour = 20 > melt_rate = 2 / 15 These lines define the number of snowballs Jay wants to make, the number of snowballs he can make per hour, and the rate at which they melt. This is correct. > answer = 0 > while num_of_snowballs > 0: > num_of_snowballs += snowballs_per_hour > num_of_snowballs -= ...

work page 2024
[27]

player judged most valuable to his team

We then find out when the band Helium was formed. Action 2: Search[When was the band "Helium" formed?] Observation 2: [] Helium / Active from 1992 Thought 3: The evidence shows that Helium was formed in 1992. Jack’s Mannequin was formed in 2004. 1992 (Helium) < 2004 (Jack’s Mannequin), so Helium was formed first. Action 3: Finish[Helium] --- Question: Wha...

work page 1992
[28]

So it’s plausible

Plausibility: The question ask for a year, and the answer "1947" is a year. So it’s plausible

work page 1947
[29]

player judged most valuable to his team

Truthfulness: Let’s search the question in google: > Search Query: What year did Maurice win the award given to the "player judged most valuable to his team" in the NHL? site: wikipedia.org > Evidence: [List of National Hockey League awards - Wikipedia] Awarded to the "player judged most valuable to his team". The original trophy was donated to the league...

work page 1947
[30]

Spark plug

Plausibility: The question asks for the name of the innovation, and the answer is " Spark plug", which is a name. So it’s plausible

work page
[31]

The evidence suggests that Prince Henry of Prussia invented a windshield wiper and the car horn

Truthfulness: Let’s search the question in google: > Search Query: Which innovation for the car was developed by Prince Henry of Prussia in 1911? site: wikipedia.org > Evidence: [Prince Henry of Prussia (1862-1929) - Wikipedia] Henry was interested in motor cars as well and supposedly invented a windshield wiper and, according to other sources, the car ho...

work page 1911
[32]

Elizabeth Perkins

Plausibility: The question asks for the name of the actor who plays wilmas mom, and the answer is "Elizabeth Perkins", which is a name. So it’s plausible

work page
[33]

Elizabeth Perkins

Truthfulness: Let’s search the question in google: > Search Query: Who played Wilma’s mom in the flintstones movie? site: wikipedia.org > Evidence: [Elizabeth Perkins - Wikipedia] Elizabeth Ann Perkins (born November 18, 1960 ) is an American actress. The evidence does not mention Wilma’s mom in the flintstones movie. Let’s search the proposed answer: > S...

work page 1960
[34]

The total wages and taxes should be positive, 22000 > 0, it’s reasonable

work page
[35]

name ’num_of_cows’ is not defined

Let’s check the code: > # Janet hires six employees. Four of them are warehouse workers who make $15/hour, and the other two are managers who make $20/hour. > num_of_warehouse_workers = 4 > num_of_managers = 2 > wage_of_warehouse_workers = 15 > wage_of_managers = 20 This defines the number of warehouse workers and managers, and their wages, this is correc...

work page 2024
[36]

NameError

The above code causes the "NameError" because it use the variable ‘ num_of_cows‘ before it is defined

work page
[37]

number_of_goats

The variable names in the code are a little bit confusing, becase both ‘num_of_pigs‘ and "number_of_goats" are used. Let’s analysis the problem, we know that the total number of animals are 56, but we don’t konw the number of pigs, cows or goats, and we can’t get any of them directly, so we can build equations to solve for the number of pigs. 56 Published...

work page 2024
[38]

The number of pizza pieces left uneaten should be a positive integer, -4.0 < 0, so the answer is not reasonable

work page
[39]

> num_of_pieces_per_pizza = 4 > num_of_pizza = 4 > num_of_pieces = num_of_pieces_per_pizza * num_of_pizza It calculates the total number of pieces ‘num_of_pieces‘, that’s correct

Let’s check the code: > # Ann, Bill, Cate, and Dale each buy personal pan pizzas cut into 4 pieces. > num_of_pieces_per_pizza = 4 > num_of_pizza = 4 > num_of_pieces = num_of_pieces_per_pizza * num_of_pizza It calculates the total number of pieces ‘num_of_pieces‘, that’s correct. > # Bill and Dale eat 50% of their pizzas and Ann and Cate eat 75% of the piz...

work page 2024
[40]

The cost of flour should be a positive number, 48 > 0, it’s reasonable

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[41]

Let’s check the code: > num_of_loaves = 12 > pounds_of_flour_per_loaf = 4 > pounds_of_flour = num_of_loaves * pounds_of_flour_per_loaf It calculates the total pounds of flour needed, that’s correct. > # A 10-pound bag of flour costs $10 and a 12-pound bag costs $13 > pounds_per_bag = 10 # ‘pounds_per_bag‘ is ambiguous since there’re two kinds of bags > co...

work page 2024
[42]

Art Imitates Life

So the answer is: 1948 Is the possible answer: (A) True (B) False The possible answer is: (B) Question: Flight that went down in the hudson river? Possible Answer: The flight that went down in the Hudson River was US Airways Flight 1549. So the answer is: US Airways Flight 1549 Is the possible answer: (A) True (B) False The possible answer is: (A) Questio...

work page 1948