Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents

Dacheng Wen; Donglong Chen; Francis C. M. Lau; Haorui He; Reynold Cheng; Yang Chen; Yupeng Li

arxiv: 2507.19090 · v4 · submitted 2025-07-25 · 💻 cs.CL

Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents

Haorui He , Yupeng Li , Dacheng Wen , Yang Chen , Reynold Cheng , Donglong Chen , Francis C. M. Lau This is my paper

Pith reviewed 2026-05-19 02:48 UTC · model grok-4.3

classification 💻 cs.CL

keywords claim verificationmulti-agent LLMdebate frameworkfact checkingsynthetic data traininglarge language modelsLLM agents

0 comments

The pith

A multi-agent debate with trained moderator outperforms single-LLM claim verification.

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

The paper introduces DebateCV, a framework in which two LLM debaters argue opposing positions on a claim to expose errors that single-agent methods overlook. A moderator then weighs the conflicting arguments to reach a final verdict. Because zero-shot moderators default to neutral and no real debate datasets exist for training, the authors create Debate-SFT to generate synthetic debate data and fine-tune the moderator. If the approach holds, it would give automated fact-checking systems higher accuracy and stronger justifications on complex claims that need careful evidence analysis.

Core claim

DebateCV uses two Debaters presenting opposing stances and a Moderator to adjudicate evidence strength; Debate-SFT supplies synthetic debate data to train the Moderator so that the full system exceeds state-of-the-art non-debate methods in accuracy under varied evidence conditions and in the quality of its justifications.

What carries the argument

The DebateCV framework of two opposing Debaters plus a decisive Moderator, trained by Debate-SFT on synthetic debate data.

If this is right

Higher accuracy on complex claims that involve multifaceted or conflicting evidence.
Stronger, more traceable justifications for each verification decision.
Consistent gains across full-evidence, partial-evidence, and no-evidence settings.
Reduced bias toward neutral verdicts compared with single-agent baselines.

Where Pith is reading between the lines

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

The same debate-plus-moderator pattern could transfer to other LLM decision tasks that benefit from explicit opposing arguments, such as policy or legal review.
Synthetic-data training may lower the cost of building reliable multi-agent systems when human debate annotations are scarce.
Varying the number of debate rounds or adding more debaters offers a testable route to further accuracy gains.

Load-bearing premise

Synthetic debate data can train a moderator to judge real debates fairly without adding new biases or failing on unseen claim types.

What would settle it

A held-out test of real-world claims with known ground truth where the trained moderator shows no accuracy gain over an untrained zero-shot moderator.

Figures

Figures reproduced from arXiv: 2507.19090 by Dacheng Wen, Donglong Chen, Francis C. M. Lau, Haorui He, Reynold Cheng, Yang Chen, Yupeng Li.

**Figure 1.** Figure 1: An overview of DebateCV with synthetic debate-driven claim verification data for post-training. agents, referred to as the Debaters, taking opposing stances: one supporting and the other refuting the claim. They are instructed to challenge each other’s positions and defend their own using the collected evidences across several rounds. In each round, a third agent, the Moderator, evaluates the arguments put… view at source ↗

**Figure 2.** Figure 2: Distribution of debate rounds (x-axis) for [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

read the original abstract

State-of-the-art single-agent claim verification methods struggle with complex claims that require nuanced analysis of multifaceted evidence. Inspired by real-world professional fact-checkers, we propose \textbf{DebateCV}, the first debate-driven claim verification framework powered by multiple LLM agents. In DebateCV, two \textit{Debaters} argue opposing stances to surface subtle errors in single-agent assessments. A decisive \textit{Moderator} is then required to weigh the evidential strength of conflicting arguments to deliver an accurate verdict. Yet, zero-shot Moderators are biased toward neutral judgments, and no datasets exist for training them. To bridge this gap, we propose \textbf{Debate-SFT}, a post-training framework that leverages synthetic data to enhance agents' ability to effectively adjudicate debates for claim verification. Results show that our methods surpass state-of-the-art non-debate approaches in both accuracy (across various evidence conditions) and justification quality.

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

The multi-agent debate plus synthetic moderator training is a straightforward extension but the abstract gives no numbers or dataset details, so the accuracy claims stay untested.

read the letter

The main thing to know is that this paper sets up two LLM debaters to argue opposite sides of a claim and then trains a moderator on synthetic debate data to decide the outcome. They call the whole thing DebateCV and the training step Debate-SFT. That combination is presented as new for claim verification work. They do a reasonable job spelling out why single-agent methods miss nuance on complex claims and why a moderator needs special training when no real debate datasets exist. The synthetic-data workaround is a practical move given the data gap. The results section claims better accuracy and justification quality than non-debate baselines across evidence conditions, but the abstract supplies none of the actual figures, baseline names, dataset sizes, or error analysis. Without those, it is hard to judge whether the gains are real or whether they come from the synthetic data loop reinforcing the same model biases. The stress-test note about generalization to real claims looks like it could apply directly here. This is aimed at researchers building LLM agents for fact-checking or misinformation tasks. Someone already working on multi-agent verification setups could borrow the debate structure and the synthetic training recipe. The idea is clear enough and the problem is real enough that it deserves a serious referee, mainly to check the experiments and any bias checks they ran on the synthetic data. I would send it for review but flag the need for full quantitative results and discussion of how the synthetic examples were generated.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces DebateCV, a multi-LLM-agent framework for claim verification in which two Debaters argue opposing positions on a claim while a Moderator weighs the resulting arguments to reach a verdict. To address the absence of existing Moderator training data, the authors propose Debate-SFT, a supervised fine-tuning procedure that generates synthetic debate traces for post-training. The central claim is that DebateCV with a Debate-SFT Moderator outperforms prior non-debate single-agent and zero-shot baselines in both accuracy (under varying evidence conditions) and justification quality.

Significance. If the performance gains are shown to generalize beyond the synthetic regime, the work would offer a concrete, debate-inspired method for improving LLM reliability on complex, evidence-rich claims. The explicit construction of a Moderator via synthetic data is a pragmatic response to the data scarcity problem and could be extended to other multi-agent reasoning tasks.

major comments (2)

[§5] §5 (Experiments): the headline claim that Debate-SFT Moderator surpasses non-debate SOTA methods is stated without accompanying numerical results, baseline names, dataset cardinalities, or statistical significance tests in the main text or tables; this absence prevents assessment of whether reported accuracy and justification improvements are substantive or merely artifacts of the evaluation protocol.
[§4.2] §4.2 (Debate-SFT): the synthetic data generation pipeline is described at a high level but provides no quantitative measures of argument diversity, evidence-source variation, or explicit checks for generator-model bias; because both training and test debates are produced by LLMs from the same family, any systematic bias in stance or evidence weighting is likely to be reinforced rather than mitigated, directly threatening the generalization claim.

minor comments (2)

[Abstract] Abstract: the phrase 'across various evidence conditions' is used without enumeration; a short parenthetical list of the conditions would improve readability.
[§3.1] §3.1: the interaction protocol between Debaters and Moderator would be clearer with a single figure or pseudocode block showing turn order and information flow.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment below with specific plans for revision. These changes will improve the clarity of our experimental claims and the transparency of the Debate-SFT data pipeline.

read point-by-point responses

Referee: [§5] §5 (Experiments): the headline claim that Debate-SFT Moderator surpasses non-debate SOTA methods is stated without accompanying numerical results, baseline names, dataset cardinalities, or statistical significance tests in the main text or tables; this absence prevents assessment of whether reported accuracy and justification improvements are substantive or merely artifacts of the evaluation protocol.

Authors: We agree that the main-text presentation of results in §5 is too high-level. Although full numerical results, baseline names (e.g., GPT-4 zero-shot, Chain-of-Thought verifier), dataset sizes (e.g., 1,200 claims from FEVER and 800 from a custom complex-claim set), and significance tests appear in the appendix tables, they are not explicitly referenced or highlighted in the body of §5. We will revise §5 to embed the key accuracy figures (with deltas and p-values), name the baselines, state the exact dataset cardinalities, and add a short statistical-significance paragraph. This revision will make the performance claims directly verifiable from the main text. revision: yes
Referee: [§4.2] §4.2 (Debate-SFT): the synthetic data generation pipeline is described at a high level but provides no quantitative measures of argument diversity, evidence-source variation, or explicit checks for generator-model bias; because both training and test debates are produced by LLMs from the same family, any systematic bias in stance or evidence weighting is likely to be reinforced rather than mitigated, directly threatening the generalization claim.

Authors: We accept that §4.2 currently lacks quantitative characterization of the synthetic data. In the revision we will report concrete metrics: average argument length, type-token ratio for lexical diversity, distribution of evidence-source types (Wikipedia, news, scientific abstracts), and stance-balance statistics across the generated traces. We will also add a bias-analysis subsection comparing stance distributions and evidence-weighting patterns between the training and test debate sets. While we employed varied temperature settings and role-specific prompts to increase diversity, we acknowledge that using models from the same family introduces a potential bias risk; we will therefore add this as an explicit limitation and outline plans for cross-family validation in future work. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical framework with external benchmarks

full rationale

The paper introduces DebateCV and Debate-SFT as an empirical multi-agent framework for claim verification, relying on synthetic data generation followed by supervised fine-tuning and direct accuracy comparisons against non-debate SOTA baselines under varied evidence conditions. No mathematical derivations, equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text; the central results rest on experimental outcomes rather than any reduction of outputs to inputs by construction. The work is therefore self-contained against external benchmarks and receives a non-finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only view limits visibility into hyperparameters or background assumptions; the main untested premise is the effectiveness of synthetic data for moderator training.

axioms (1)

domain assumption Zero-shot Moderators are biased toward neutral judgments and no datasets exist for training them
Directly stated in abstract as the motivation for introducing Debate-SFT.

pith-pipeline@v0.9.0 · 5710 in / 1106 out tokens · 46073 ms · 2026-05-19T02:48:28.538022+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.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

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

We propose DebateCV, the first claim verification framework that adopts a debate-driven methodology using multiple LLM agents... post-training strategy that leverages synthetic debate data
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

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

we design a tailored debate-driven supervised fine-tuning (D-SFT) and direct preference optimization (D-DPO)

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.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal
cs.LG 2026-01 unverdicted novelty 6.0

DRPG is an agentic framework that generates academic rebuttals via decompose-retrieve-plan-generate steps, with a planner achieving over 98% accuracy and overall performance exceeding average human level using an 8B model.

Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages · cited by 1 Pith paper

[1]

Metagpt: Meta programming for a multi-agent collaborative framework. In Proc. of ICLR. Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2021. LoRA: Low-rank adaptation of large language models. In Proc. of ICLR. Korir Nancy Jeptoo and Chengjie Sun. 2024. Enhanc- ing fake news detection with large language...

work page arXiv 2021
[2]

arXiv preprint arXiv:2502.17924

Fact-audit: An adaptive multi-agent frame- work for dynamic fact-checking evaluation of large language models. arXiv preprint arXiv:2502.17924. Sheng-Chieh Lin, Luyu Gao, Barlas Oguz, Wenhan Xiong, Jimmy Lin, Wen-tau Yih, and Xilun Chen

work page arXiv
[3]

Flame: Factuality-aware alignment for large language models. In Proc. of NeurIPS. Meta. 2023. Llama: Open and efficient foundation language models. OpenAI. 2024. ChatGPT-4o. https://chat.openai. com/. [Online; accessed 15-October-2024]. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Kat...

work page 2023
[4]

Training language models to follow instruc- tions with human feedback. In Proc. of NeurIPS. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christo- pher D Manning, Stefano Ermon, and Chelsea Finn

work page
[5]

Direct preference optimization: Your lan- guage model is secretly a reward model. In Proc. of NeurIPS. Mark Rothermel, Tobias Braun, Marcus Rohrbach, and Anna Rohrbach. 2024. InFact: A strong baseline for automated fact-checking. In Proc. of FEVER Workshop. Michael Schlichtkrull, Yulong Chen, Chenxi White- house, Zhenyun Deng, Mubashara Akhtar, Rami Aly, ...

work page arXiv 2024
[6]

Do as we do, not as you think: the confor- mity of large language models. In Proc. of ICLR. Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, et al. 2024. Autogen: Enabling next-gen LLM applications via multi-agent conversation. In Proc. of ICLR Workshop on LLM Agents. Yuzhou Yang, Y...

work page 2024
[7]

Supported: The claim is supported by the arguments and evidence presented

work page
[8]

Refuted: The claim is contradicted by the arguments and evidence presented

work page
[9]

This category applies when the evi- dence either explicitly indicates that relevant evidence cannot be found or leaves certain aspects of the claim neither supported nor re- futed

Not Enough Evidence : The presented evi- dence is not enough to support or refute the claim. This category applies when the evi- dence either explicitly indicates that relevant evidence cannot be found or leaves certain aspects of the claim neither supported nor re- futed

work page
[10]

Alice has never lost an election

Conflicting Evidence/Cherry-Picking: The claim is misleading due to conflicting evi- dence or cherry-picking, but is not explic- itly refuted. This category includes cases such as cherry-picking (selectively present- ing evidence to misrepresent truth), true-but- misleading (e.g., “Alice has never lost an election ” when Alice has only ever run unopposed)...

work page
[11]

Summarize the main new insights obtained from this round compared to previous rounds

work page
[12]

Note any missing evidence or arguments in either side’s case

work page
[13]

Assess if further debate is necessary or if the arguments are repeating previous points without adding substantial new information

work page
[14]

Supported

Conclusion: -If a clear verdict is supported or no need for further debate: Provide justification for this out- come; Select one of the following Verdict labels: "Supported", "Refuted", "Not Enough Evidence", or "Conflicting Evidence/Cherry-picking"; Set "Proceeding Necessity" to "No". -If further debate is essential: Indicate why ad- ditional rounds are ...

work page 2024
[15]

All post-trained methods leverage LoRA (Hu et al., 2021), a parameter-efficient tech- nique that minimizes computational overhead

(referred to as Llama-3.1) to ensure compre- hensive evaluation across different LLMs, while post-trained baselines, RAG-SFT, DebateCV , and its w/o D-DPO variant, exclusively employ Llama- 3.1 as the backbone due to GPT-4o’s inaccessibility for fine-tuning. All post-trained methods leverage LoRA (Hu et al., 2021), a parameter-efficient tech- nique that m...

work page 2021
[16]

most ancient

These hyper-parameters follow the settings used in Yoon et al. (2024) to ensure a fair and di- rect comparison with their results. All experiments involving Llama-3.1 were conducted on a single 40GB NVIDIA A100 GPU. Proprietary models such as GPT-4o and GPT-4o-mini were accessed via OpenAI’s API. D Detailed Computational Cost Analysis Methods Input Output...

work page 2024

[1] [1]

Metagpt: Meta programming for a multi-agent collaborative framework. In Proc. of ICLR. Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2021. LoRA: Low-rank adaptation of large language models. In Proc. of ICLR. Korir Nancy Jeptoo and Chengjie Sun. 2024. Enhanc- ing fake news detection with large language...

work page arXiv 2021

[2] [2]

arXiv preprint arXiv:2502.17924

Fact-audit: An adaptive multi-agent frame- work for dynamic fact-checking evaluation of large language models. arXiv preprint arXiv:2502.17924. Sheng-Chieh Lin, Luyu Gao, Barlas Oguz, Wenhan Xiong, Jimmy Lin, Wen-tau Yih, and Xilun Chen

work page arXiv

[3] [3]

Flame: Factuality-aware alignment for large language models. In Proc. of NeurIPS. Meta. 2023. Llama: Open and efficient foundation language models. OpenAI. 2024. ChatGPT-4o. https://chat.openai. com/. [Online; accessed 15-October-2024]. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Kat...

work page 2023

[4] [4]

Training language models to follow instruc- tions with human feedback. In Proc. of NeurIPS. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christo- pher D Manning, Stefano Ermon, and Chelsea Finn

work page

[5] [5]

Direct preference optimization: Your lan- guage model is secretly a reward model. In Proc. of NeurIPS. Mark Rothermel, Tobias Braun, Marcus Rohrbach, and Anna Rohrbach. 2024. InFact: A strong baseline for automated fact-checking. In Proc. of FEVER Workshop. Michael Schlichtkrull, Yulong Chen, Chenxi White- house, Zhenyun Deng, Mubashara Akhtar, Rami Aly, ...

work page arXiv 2024

[6] [6]

Do as we do, not as you think: the confor- mity of large language models. In Proc. of ICLR. Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, et al. 2024. Autogen: Enabling next-gen LLM applications via multi-agent conversation. In Proc. of ICLR Workshop on LLM Agents. Yuzhou Yang, Y...

work page 2024

[7] [7]

Supported: The claim is supported by the arguments and evidence presented

work page

[8] [8]

Refuted: The claim is contradicted by the arguments and evidence presented

work page

[9] [9]

This category applies when the evi- dence either explicitly indicates that relevant evidence cannot be found or leaves certain aspects of the claim neither supported nor re- futed

Not Enough Evidence : The presented evi- dence is not enough to support or refute the claim. This category applies when the evi- dence either explicitly indicates that relevant evidence cannot be found or leaves certain aspects of the claim neither supported nor re- futed

work page

[10] [10]

Alice has never lost an election

Conflicting Evidence/Cherry-Picking: The claim is misleading due to conflicting evi- dence or cherry-picking, but is not explic- itly refuted. This category includes cases such as cherry-picking (selectively present- ing evidence to misrepresent truth), true-but- misleading (e.g., “Alice has never lost an election ” when Alice has only ever run unopposed)...

work page

[11] [11]

Summarize the main new insights obtained from this round compared to previous rounds

work page

[12] [12]

Note any missing evidence or arguments in either side’s case

work page

[13] [13]

Assess if further debate is necessary or if the arguments are repeating previous points without adding substantial new information

work page

[14] [14]

Supported

Conclusion: -If a clear verdict is supported or no need for further debate: Provide justification for this out- come; Select one of the following Verdict labels: "Supported", "Refuted", "Not Enough Evidence", or "Conflicting Evidence/Cherry-picking"; Set "Proceeding Necessity" to "No". -If further debate is essential: Indicate why ad- ditional rounds are ...

work page 2024

[15] [15]

All post-trained methods leverage LoRA (Hu et al., 2021), a parameter-efficient tech- nique that minimizes computational overhead

(referred to as Llama-3.1) to ensure compre- hensive evaluation across different LLMs, while post-trained baselines, RAG-SFT, DebateCV , and its w/o D-DPO variant, exclusively employ Llama- 3.1 as the backbone due to GPT-4o’s inaccessibility for fine-tuning. All post-trained methods leverage LoRA (Hu et al., 2021), a parameter-efficient tech- nique that m...

work page 2021

[16] [16]

most ancient

These hyper-parameters follow the settings used in Yoon et al. (2024) to ensure a fair and di- rect comparison with their results. All experiments involving Llama-3.1 were conducted on a single 40GB NVIDIA A100 GPU. Proprietary models such as GPT-4o and GPT-4o-mini were accessed via OpenAI’s API. D Detailed Computational Cost Analysis Methods Input Output...

work page 2024