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arxiv: 2308.07201 · v1 · submitted 2023-08-14 · 💻 cs.CL

Recognition: 2 theorem links

· Lean Theorem

ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate

Chi-Min Chan , Weize Chen , Yusheng Su , Jianxuan Yu , Wei Xue , Shanghang Zhang , Jie Fu , Zhiyuan Liu
Authors on Pith no claims yet

Pith reviewed 2026-05-13 12:58 UTC · model grok-4.3

classification 💻 cs.CL
keywords multi-agent debateLLM evaluationtext assessmentNLG tasksautomated evaluationChatEvalhuman-mimicking evaluationopen-ended questions
0
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The pith

A multi-agent team of LLMs debates to evaluate generated text with human-like reliability.

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

The paper argues that single large language models fall short of human evaluators when scoring the quality of text outputs. It introduces a multi-agent debate framework in which several LLMs discuss and critique responses to open-ended questions and standard NLG tasks. The approach draws on the practice of human evaluation panels that use multiple annotators for better consensus. If the method works, automated evaluation becomes more consistent and less reliant on expensive human labor while handling nuanced judgments. Experiments show the resulting system, ChatEval, produces assessments that better align with human standards than isolated model scoring.

Core claim

ChatEval constructs a multi-agent referee team that allows a group of LLMs to autonomously discuss and evaluate the quality of generated responses from different models on open-ended questions and traditional natural language generation tasks, transcending mere textual scoring to offer a human-mimicking evaluation process for reliable assessments.

What carries the argument

The multi-agent referee team that lets distinct LLMs exchange views and reach consensus on response quality.

If this is right

  • Evaluations on open-ended questions become more reliable without added human annotators.
  • Standard NLG tasks receive assessments that capture subtleties single models often miss.
  • The framework scales to intricate tasks by combining multiple models' strengths.
  • Labor and time costs for large-scale text evaluation drop while consistency rises.

Where Pith is reading between the lines

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

  • The debate logs could serve as richer training signals for improving the evaluated models themselves.
  • Similar multi-agent structures might transfer to other LLM workflows such as planning or verification.
  • Optimal team composition and discussion length remain open parameters that future runs could tune.

Load-bearing premise

Performance gains come from genuine collaboration among the agents rather than from simply making more calls to one model or using better single-prompt instructions.

What would settle it

A controlled test in which one LLM receives the full transcript of the multi-agent discussion and produces evaluation scores that match or exceed the team's accuracy against human judgments.

read the original abstract

Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost. With the emergence of large language models (LLMs), researchers have explored LLMs' potential as alternatives for human evaluation. While these single-agent-based approaches show promise, experimental results suggest that further advancements are needed to bridge the gap between their current effectiveness and human-level evaluation quality. Recognizing that best practices of human evaluation processes often involve multiple human annotators collaborating in the evaluation, we resort to a multi-agent debate framework, moving beyond single-agent prompting strategies. The multi-agent-based approach enables a group of LLMs to synergize with an array of intelligent counterparts, harnessing their distinct capabilities and expertise to enhance efficiency and effectiveness in handling intricate tasks. In this paper, we construct a multi-agent referee team called ChatEval to autonomously discuss and evaluate the quality of generated responses from different models on open-ended questions and traditional natural language generation (NLG) tasks. Our analysis shows that ChatEval transcends mere textual scoring, offering a human-mimicking evaluation process for reliable assessments. Our code is available at https://github.com/chanchimin/ChatEval.

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.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces ChatEval, a multi-agent debate framework in which multiple LLMs assume distinct referee roles, discuss, and collectively evaluate the quality of responses generated by other models on open-ended questions and standard NLG tasks. It claims that this collaborative process yields more reliable, human-mimicking assessments than conventional single-agent LLM prompting, with supporting experiments and publicly released code.

Significance. If the reported gains can be shown to arise specifically from agent interaction and role differentiation rather than from increased total inference budget, the work would offer a practical, scalable method for automated evaluation that reduces dependence on human annotators while preserving reliability. The public code release is a clear strength for reproducibility and follow-up research.

major comments (2)
  1. [Section 4] Experimental setup (Section 4): No control condition equates total LLM calls or token budget between ChatEval and single-agent baselines. A single-agent variant that issues the same number of sequential or repeated calls (with concatenated history) is required to isolate whether improvements stem from multi-agent debate structure rather than simply aggregating more model outputs.
  2. [Section 4] Results and analysis: The abstract asserts that ChatEval provides superior human-mimicking evaluation, yet the description supplies no concrete quantitative metrics (e.g., Pearson/Spearman correlation with human judgments, win rates, or statistical significance tests) or explicit single-agent baselines with matched compute. This leaves the central superiority claim only moderately supported.
minor comments (2)
  1. [Section 3] The roles and interaction protocol of the referee team are described at a high level; a concise diagram or pseudocode of one debate round would improve clarity of the multi-agent mechanism.
  2. [Section 2] Related-work discussion could more explicitly contrast ChatEval with prior multi-agent LLM frameworks (e.g., those using debate for reasoning) to highlight the novelty of the evaluation-specific application.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments, which have helped us improve the clarity and rigor of our experimental analysis. We address each major comment below.

read point-by-point responses

  1. Referee: [Section 4] Experimental setup (Section 4): No control condition equates total LLM calls or token budget between ChatEval and single-agent baselines. A single-agent variant that issues the same number of sequential or repeated calls (with concatenated history) is required to isolate whether improvements stem from multi-agent debate structure rather than simply aggregating more model outputs.

    Authors: We acknowledge the importance of controlling for the total computational budget to ensure that the observed improvements are due to the multi-agent debate mechanism rather than increased inference calls. In the revised manuscript, we introduce a new single-agent baseline that performs an equivalent number of sequential LLM calls with concatenated history. Our updated experiments demonstrate that ChatEval maintains superior performance even under this matched-budget condition, thereby strengthening the evidence for the benefits of multi-agent interaction. revision: yes

  2. Referee: [Section 4] Results and analysis: The abstract asserts that ChatEval provides superior human-mimicking evaluation, yet the description supplies no concrete quantitative metrics (e.g., Pearson/Spearman correlation with human judgments, win rates, or statistical significance tests) or explicit single-agent baselines with matched compute. This leaves the central superiority claim only moderately supported.

    Authors: We appreciate this feedback on the presentation of results. While the full manuscript in Section 4 does include quantitative comparisons and human correlation metrics, we have revised the abstract to explicitly state key quantitative findings, including Pearson and Spearman correlations with human judgments, win rates against baselines, and statistical significance. Additionally, as noted in response to the first comment, we now include matched-compute single-agent baselines. These changes provide stronger support for the superiority claim. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical multi-agent evaluation framework

full rationale

The paper presents ChatEval as an empirical construction: a multi-agent debate setup for LLM-based evaluation on open-ended and NLG tasks. No equations, derivations, or fitted parameters appear in the provided text. Claims rest on experimental comparisons and public code rather than any self-referential reduction where a 'prediction' equals an input by construction. Self-citations are absent from the abstract and setup; the method does not invoke uniqueness theorems or smuggle ansatzes. This is a standard empirical proposal whose validity can be checked externally via replication, yielding a score of 0.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework rests on the domain assumption that LLMs can productively debate evaluation criteria and that the resulting consensus is more reliable than single-model output. No free parameters are explicitly fitted in the abstract; the main invented entity is the ChatEval referee team itself.

axioms (1)
  • domain assumption LLMs can effectively debate and reach consensus on text quality
    Invoked when the multi-agent framework is introduced as superior to single-agent methods.
invented entities (1)
  • ChatEval referee team no independent evidence
    purpose: Autonomous multi-agent discussion and evaluation of generated responses
    New system constructed in the paper; no independent evidence provided beyond the authors' experiments.

pith-pipeline@v0.9.0 · 5517 in / 1123 out tokens · 31435 ms · 2026-05-13T12:58:17.427214+00:00 · methodology

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    Relation between the paper passage and the cited Recognition theorem.

    Our analysis shows that ChatEval transcends mere textual scoring, offering a human-mimicking evaluation process for reliable assessments

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Reference graph

Works this paper leans on

28 extracted references · 28 canonical work pages · cited by 28 Pith papers · 8 internal anchors

  1. [1]

    Benchmarking foundation models with language-model-as-an-examiner

    Yushi Bai, Jiahao Ying, Yixin Cao, Xin Lv, Yuze He, Xiaozhi Wang, Jifan Yu, Kaisheng Zeng, Yijia Xiao, Haozhe Lyu, et al. Benchmarking foundation models with language-model-as-an-examiner. arXiv preprint arXiv:2306.04181,

    work page arXiv

  2. [2]

    Language models are few-shot learners

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901,

    work page 1901

  3. [3]

    Fast, cheap, and creative: Evaluating translation quality using amazon’s mechanical turk

    Chris Callison-Burch. Fast, cheap, and creative: Evaluating translation quality using amazon’s mechanical turk. In Proceedings of the 2009 conference on empirical methods in natural language processing, pp. 286–295,

    work page 2009

  4. [5]

    Cheng-Han Chiang and Hung-yi Lee

    URL https://arxiv.org/abs/2006.14799. Cheng-Han Chiang and Hung-yi Lee. Can large language models be an alternative to human evalu- ations? arXiv preprint arXiv:2305.01937,

    work page arXiv 2006

  5. [6]

    Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality

    11 Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. See https://vicuna. lmsys. org (accessed 14 April 2023),

    work page 2023

  6. [7]

    BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805,

    work page internal anchor Pith review Pith/arXiv arXiv

  7. [8]

    Improving Factuality and Reasoning in Language Models through Multiagent Debate

    Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch. Improv- ing factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325,

    work page internal anchor Pith review Pith/arXiv arXiv

  8. [9]

    Findings of the Association for Computa- tional Linguistics: ACL-IJCNLP 2021

    Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu. Gptscore: Evaluate as you desire.arXiv preprint arXiv:2302.04166,

    work page arXiv

  9. [10]

    Human-like summarization evaluation with chatgpt

    Mingqi Gao, Jie Ruan, Renliang Sun, Xunjian Yin, Shiping Yang, and Xiaojun Wan. Human-like summarization evaluation with chatgpt. arXiv preprint arXiv:2304.02554,

    work page arXiv

  10. [11]

    The perils of using mechanical turk to evaluate open-ended text generation

    Marzena Karpinska, Nader Akoury, and Mohit Iyyer. The perils of using mechanical turk to evaluate open-ended text generation. arXiv preprint arXiv:2109.06835,

    work page arXiv

  11. [12]

    CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

    Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. Camel: Communicative agents for” mind” exploration of large scale language model society. arXiv preprint arXiv:2303.17760, 2023a. Ruosen Li, Teerth Patel, and Xinya Du. Prd: Peer rank and discussion improve large language model based evaluations. arXiv preprint arX...

    work page internal anchor Pith review arXiv

  12. [13]

    Training socially aligned language models in simulated human society

    Ruibo Liu, Ruixin Yang, Chenyan Jia, Ge Zhang, Denny Zhou, Andrew M Dai, Diyi Yang, and Soroush V osoughi. Training socially aligned language models in simulated human society.arXiv preprint arXiv:2305.16960, 2023a. Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. Gpteval: Nlg evaluation using gpt-4 with better human alignment...

    work page arXiv

  13. [14]

    Roco: Dialectic multi-robot collaboration with large language models

    Zhao Mandi, Shreeya Jain, and Shuran Song. Roco: Dialectic multi-robot collaboration with large language models. arXiv preprint arXiv:2307.04738,

    work page arXiv

  14. [15]

    Usr: An unsupervised and reference free evaluation metric for dialog generation

    Shikib Mehri and Maxine Eskenazi. Usr: An unsupervised and reference free evaluation metric for dialog generation. arXiv preprint arXiv:2005.00456,

    work page arXiv 2005

  15. [16]

    Why we need new evaluation metrics for NLG

    Jekaterina Novikova, Ond ˇrej Du ˇsek, Amanda Cercas Curry, and Verena Rieser. Why we need new evaluation metrics for NLG. In Proceedings of the 2017 Conference on Empirical Meth- ods in Natural Language Processing , pp. 2241–2252, Copenhagen, Denmark, September

    work page 2017

  16. [17]

    Why We Need New Evaluation Metrics for NLG

    Association for Computational Linguistics. doi: 10.18653/v1/D17-1238. URL https:// aclanthology.org/D17-1238. 12 Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Inform...

    work page doi:10.18653/v1/d17-1238

  17. [18]

    Generative Agents: Interactive Simulacra of Human Behavior

    Joon Sung Park, Joseph C O’Brien, Carrie J Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. Generative agents: Interactive simulacra of human behavior. arXiv preprint arXiv:2304.03442,

    work page internal anchor Pith review Pith/arXiv arXiv

  18. [19]

    ChatDev: Communicative Agents for Software Development

    Chen Qian, Xin Cong, Cheng Yang, Weize Chen, Yusheng Su, Juyuan Xu, Zhiyuan Liu, and Maosong Sun. Communicative agents for software development. arXiv preprint arXiv:2307.07924,

    work page internal anchor Pith review Pith/arXiv arXiv

  19. [20]

    Multitask Prompted Training Enables Zero-Shot Task Generalization

    Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, An- toine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. Multitask prompted training enables zero-shot task generalization. arXiv preprint arXiv:2110.08207,

    work page internal anchor Pith review arXiv

  20. [21]

    Bleurt: Learning robust metrics for text gener- ation

    Thibault Sellam, Dipanjan Das, and Ankur P Parikh. Bleurt: Learning robust metrics for text gener- ation. arXiv preprint arXiv:2004.04696,

    work page arXiv 2004

  21. [22]

    Are large language models good evalu- ators for abstractive summarization? arXiv preprint arXiv:2305.13091,

    Chenhui Shen, Liying Cheng, Yang You, and Lidong Bing. Are large language models good evalu- ators for abstractive summarization? arXiv preprint arXiv:2305.13091,

    work page arXiv

  22. [23]

    Is chatgpt a good nlg evaluator? a preliminary study

    Jiaan Wang, Yunlong Liang, Fandong Meng, Haoxiang Shi, Zhixu Li, Jinan Xu, Jianfeng Qu, and Jie Zhou. Is chatgpt a good nlg evaluator? a preliminary study. arXiv preprint arXiv:2303.04048, 2023a. Peiyi Wang, Lei Li, Liang Chen, Dawei Zhu, Binghuai Lin, Yunbo Cao, Qi Liu, Tianyu Liu, and Zhifang Sui. Large language models are not fair evaluators. arXiv pre...

    work page arXiv

  23. [24]

    Jason Weston and Sainbayar Sukhbaatar

    Ning Wu, Ming Gong, Linjun Shou, Shining Liang, and Daxin Jiang. Large language models are diverse role-players for summarization evaluation. arXiv preprint arXiv:2303.15078,

    work page arXiv

  24. [25]

    BERTScore: Evaluating Text Generation with BERT

    13 Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. Bertscore: Evaluat- ing text generation with bert. arXiv preprint arXiv:1904.09675,

    work page internal anchor Pith review Pith/arXiv arXiv 1904

  25. [26]

    Mover- score: Text generation evaluating with contextualized embeddings and earth mover distance

    Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M Meyer, and Steffen Eger. Mover- score: Text generation evaluating with contextualized embeddings and earth mover distance. arXiv preprint arXiv:1909.02622,

    work page arXiv 1909

  26. [27]

    Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

    Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging llm-as-a-judge with mt-bench and chatbot arena. arXiv preprint arXiv:2306.05685,

    work page internal anchor Pith review Pith/arXiv arXiv

  27. [28]

    Towards a unified multi-dimensional evaluator for text generation.arXiv preprint arXiv:2210.07197,

    Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, and Jiawei Han. Towards a unified multi-dimensional evaluator for text generation.arXiv preprint arXiv:2210.07197,

    work page arXiv

  28. [29]

    (2023) and design several different role descriptions as follows

    A PROMPT TEMPLATE AND DIVERSE ROLE PROMPT The overall prompt template is shown in Table 6, we draw inspiration from Wu et al. (2023) and design several different role descriptions as follows. General Public You are now General Public, one of the referees in this task. You are interested in the story and looking for updates on the investigation. Please thi...

    work page 2023