Beyond Single Models: Enhancing LLM Detection of Ambiguity in Requests through Debate

Ana Davila; Jacinto Colan; Yasuhisa Hasegawa

REVIEW 4 major objections 6 minor 19 references

Beyond Single Models: Enhancing LLM Detection of Ambiguity in Requests through Debate

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A leader-follower debate among three small language models improves ambiguity detection for two of them, lifting Mistral-7B from 28.3% to 76.7% success, while degrading the strongest single model.

desk verdict The paper asks a sensible question but never defines what "success" means, so the headline 76.7% is not interpretable and the central comparison collapses. read the letter →

arxiv 2507.12370 v1 pith:DB5QXJSE submitted 2025-07-16 cs.CL cs.HC

Ana Davila , Jacinto Colan , Yasuhisa Hasegawa This is my paper

classification cs.CLcs.HC

keywords largelanguagemodelsambiguitydetectionmulti-agentdebateleader-followerprotocolhuman-robotinteractionclarificationquestionssmall

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper asks whether a structured debate among several small language models can catch ambiguities in user instructions that any single model would miss. It reports that a leader-follower debate protocol markedly improves two of the three models tested: Llama3-8B rises from 13.3% to 40.0% success and Mistral-7B from 28.3% to 76.7%, with Mistral-led debates reaching consensus quickly and handling spatial ambiguity especially well. The same protocol hurts the strongest single model: Gemma2-9B alone scores 80.0%, while its debates score 48.3%. The authors conclude that debate is a model-dependent, targeted enhancement rather than a universal fix, and that the temporal cost of roughly 22 to 28 seconds per debate matters for real-time use.

What carries the argument

The leader-follower debate protocol is the mechanism that carries the argument. Three agents, one leader and two followers, rotate roles on every instruction; the leader first proposes a verdict of 'clear' or a single clarifying question, each follower independently says 'Agree' or 'Disagree' and may offer an alternative question, and if both followers agree the debate ends. Otherwise the leader integrates the feedback and produces a revised proposal, repeating up to five rounds. This machinery forces the leader to incorporate dissenting views, so the final output represents a consensus among independent agents rather than one model's first guess, which is why the authors attribute the accuracy gains to the structure of the exchange.

What would settle it

Run the same three models on the same dataset with a pre-specified scoring rule that requires the final clarifying question to exactly match the gold-standard question for each ambiguous instruction and requires a 'clear' verdict on the unambiguous counterparts; if Mistral-7B-led debate no longer beats its single-agent baseline under that rule, the paper's central comparison collapses.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that a leader-follower multi-agent debate can extract better ambiguity judgments from small language models than the same models acting alone, and that the gain is conditional on which model leads. The evidence is a programmatically generated set of instructions with numerical, attribute, and spatial ambiguities, each paired with an unambiguous counterpart. Under the paper's reported but unspecified scoring rule, debate raises Llama3-8B from 13.3% to 40.0% and Mistral-7B from 28.3% to 76.7%, with Mistral-7B-led debates reaching consensus in 98.3% of runs and averaging 22.5 seconds; the same protocol drops Gemma2-9B from 80.0% to 48.3%. The authors read this as evidence that debate is a targeted enhancement tool for weaker agents, not a universal improvement.

Load-bearing premise

The central result depends on an unstated scoring rule: the paper never specifies whether a successful trial is one that flags any ambiguity, one whose clarifying question matches a gold-standard reference, or one that correctly declares a clean instruction clear, and the comparison between 76.7% and 28.3% changes meaning with that choice.

Editorial extensions

If this is right

For small open-weights models with low solo accuracy, structured debate can bring ambiguity detection close to or beyond a stronger single model: Mistral-7B-led debate reaches 76.7%, near Gemma2-9B's solo 80.0%.
Debate is not universally beneficial; Gemma2-9B loses accuracy when forced into the protocol, so deployment should be selective and model-aware.
Consensus dynamics carry useful signal: Mistral-7B-led debates reach consensus 98.3% of the time, mostly by round 2, and the paper reports that consensus outcomes are high-quality with 95% success when Mistral-7B leads.
The added latency of roughly 22.5 to 28.1 seconds is a real cost for human-robot interaction, and the paper itself points to adaptive triggers that invoke debate only when needed.
The debate outcome is a direct corollary of the claim that collaborative scrutiny beats individual reasoning for weaker models, which is exactly what a real-time system would rely on.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable implication beyond the paper is that the debate gain comes mostly from detecting that ambiguity exists, not from generating better clarifying questions, since the protocol forces the leader to acknowledge disagreement before refining a question.
The consensus rate could be reused as a confidence signal: instructions that drive persistent disagreement are the ones most likely to be genuinely ambiguous, so a system could trigger debate only for those cases and save latency on the rest.
The Gemma2-9B degradation suggests a ceiling effect: when the leading model is already right, follower dissent may be noise; a natural extension would let a high-confidence leader override followers instead of always revising.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The multi-agent debate setup for ambiguity detection is a reasonable extension of prior work, and the paper deserves credit for testing three small LLMs, rotating leader roles, and acknowledging that debate helps some models and hurts a strong one. The related work is honest about the connection to Madisse and LLM-Consensus. The synthetic dataset generation is described clearly enough for three ambiguity types, though the dataset size is never stated.

The problem is load-bearing: Section 4 reports success rates but never defines what a successful trial is. Does the model need to flag ambiguity? Does its clarifying question need to match a gold standard? Are “clear” verdicts on unambiguous instructions counted? The paper doesn’t say. Without that, the single-vs-debate comparison is not computable. The stress-test note is right that this is fatal to the empirical claim. A model that correctly says “ambiguous” but asks an off-target question could pass one scoring rule and fail another, so the reported 13.3%, 76.7%, and 80.0% are uninterpretable.

There’s also a direct numerical contradiction: Section 4.3 puts Mistral’s non-consensus rate at 1.7%, but Table 1 says Mistral reaches consensus 90% of the time, implying a 10% non-consensus rate. Similarly, Llama3-8B text says 31.7% non-consensus while Table 1 implies 15%. That inconsistency suggests the evaluation pipeline was not stable across runs.

No error bars, no significance tests, and no released data or code further weaken the empirical support. The paper’s own framing—debate helps weaker models, hurts a stronger one—is plausible and consistent with other multi-agent results, but as reported, it’s not backed by a verifiable scoring protocol.

The paper would be more believable as a workshop contribution than as a citable result. If the authors define the scoring rule, describe how non-consensus trials are treated, release data and code, and fix the numerical inconsistency, it could be a modest but useful application note for HRI researchers using local LLMs. As it stands, a serious referee would need the missing information before trusting any of the percentages. I would not send this to peer review in its current form; I’d desk reject with an invitation to resubmit after major revisions and a complete evaluation description.

Referee Report

4 major / 6 minor

Summary. The paper proposes and evaluates a leader-follower multi-agent debate framework for detecting ambiguity in user instructions and generating clarifying questions, using three small open-weight LLMs (Llama3-8B, Gemma2-9B, Mistral-7B) on a programmatically generated dataset of numerical, attribute, and spatial ambiguities. The authors compare single-agent baselines with debates in which one model leads and two others critique, rotating the leader role, and report success rates, consensus rates, and debate latency. The main claim is that debate substantially improves performance for Llama3-8B and Mistral-7B, with Mistral-led debates reaching 76.7% success, while degrading the already strong single-agent Gemma2-9B (80.0% single vs. 48.3% debate).

Significance. If the reported results hold, the paper would provide a useful empirical data point on when multi-agent debate helps or hurts small LLMs for ambiguity detection in human-robot interaction, and its use of a controlled programmatic dataset and role rotation is a reasonable methodological start. However, the central evaluation metric is never operationalized, the dataset size is absent, and the consensus statistics are internally inconsistent, so the headline comparison between single and debate conditions is not currently computable from the manuscript. These are fixable but essential gaps, and the manuscript also lacks any error bars or significance testing, which is especially important given the stochastic nature of LLM decoding and the small reported effect sizes.

major comments (4)

[Section 3.3 and Section 4.1] The evaluation metric 'success' is never defined. Section 3.3 specifies that an agent must either state that the instruction is clear or propose a single clarifying question, but Section 4.1 reports success rates (e.g., 80.0% for single Gemma2-9B, 76.7% for Debate(Mistral-7B)) without stating whether a successful trial requires correct ambiguity detection only, detection plus any clarifying question, or detection plus a question matching a gold-standard question, and without specifying how unambiguous control items are scored. This makes the central single-versus-debate comparison non-computable from the manuscript.
[Section 3.1] The dataset size is not reported. The text says 'a specified number of unique examples' but never gives the total number of instructions or the per-type counts for numerical, attribute, and spatial ambiguity. All percentages in Section 4 therefore lack denominators, and no error bars or confidence intervals are provided anywhere in Section 4, so the reader cannot assess the precision of any reported success rate.
[Section 4.3 and Table 1] The consensus statistics are internally inconsistent. Section 4.3 states that Mistral-7B-led debates had a non-consensus rate of 1.7% (i.e., a consensus rate of 98.3%), while Table 1 reports a consensus reach of 90% for Mistral-7B. Moreover, with a consensus-outcome success rate of 95% from Table 1, neither a 90% nor a 98.3% consensus rate reconciles with the overall Debate(Mistral-7B) success rate of 76.7% under standard assumptions that non-consensus trials are scored as failures. Please state explicitly whether non-consensus trials are counted as failures, excluded from the denominator, or scored using the leader's last proposal, and correct the numerical conflict.
[Section 4.2] The claims of improvement or degradation, such as Mistral-7B's spatial ambiguity detection rising from 10% to 75% under debate, are based on single runs with no variance or significance testing. Given that the models are stochastic and the sample sizes are unreported, these differences may be within noise. The authors should report multiple independent runs or bootstrap confidence intervals for all headline percentages in Section 4.

minor comments (6)

[References] There is a typographical error in the reference list: 'V ol' should be 'Vol' in multiple entries, and the same typo appears in the text.
[Section 3.3.2] The text has a spacing typo: 'F ollower' should be 'Follower'.
[Section 5] The conclusion contains a spacing typo: '13. 3%' should be '13.3%'.
[Section 4.2] The approximate percentages in the text, such as 'attribute ( 70%), numerical ( 85%), and spatial ( 85%)', should be replaced with exact values or a table, and the spacing should be corrected.
[Figures 2-7] The manuscript references Figures 2 through 7 with captions, but the figure images are not visible in the submitted text; please ensure all plots are embedded or provide the data tables underlying them.
[Section 3.3.2] It is unclear whether follower responses are shared with the other follower or only with the leader; specify the information flow between followers, since this affects the debate dynamics.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the debate-versus-single-agent comparison is a measured empirical result with no fitted parameters, no derivation from inputs, and no load-bearing self-citation.

full rationale

This paper is an empirical measurement study rather than a derivation. Its central claims (e.g., 'Mistral-7B-led debates achieving a notable 76.7% success rate' versus a 28.3% single-agent baseline, and Gemma2-9B dropping from 80.0% to 48.3% under debate) rest on running three LLMs on a programmatically generated dataset in single-agent and leader-follower debate conditions. No parameter is fitted to a subset of the data and then renamed a prediction; no equation defines one reported quantity in terms of another; and the debate protocol in Section 3.3.2 is described in situ and can terminate in consensus, non-consensus, or error, so the outcome is not forced by construction. The self-citations (Refs [1], [4], [9], [18]) are background references for HRI motivation and prior ambiguity-detection work; none of the load-bearing experimental claims depends on an external result by the same authors, and no uniqueness theorem or imported ansatz is invoked. The manuscript-level defects are real but belong to correctness and reproducibility, not circularity: Section 4 never defines the success criterion (ambiguity-only detection, any clarifying question, or gold-standard question match) or how non-consensus trials enter the scoring denominator, which makes the single-vs-debate comparison non-computable from the text; Section 4.3's 1.7% non-consensus rate for Mistral-7B contradicts Table 1's 90% consensus reach (roughly 10% non-consensus); and Section 4's note that 'The figures mentioned are assumed to graphically represent the specific data values previously detailed' flags that the plotted evidence may be absent from this version. None of these reduces a claimed result to its own inputs, so the appropriate circularity finding, per the hard rules, is no significant circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The ledger contains only hand-set hyperparameters and domain assumptions; no new theoretical entities or fitted model parameters are introduced.

free parameters (4)

temperature = 0.5
Fixed for all agents; no sensitivity analysis, so results may depend on this choice.
max_tokens = 350
Response length cap; could truncate reasoning and affect output quality.
max_rounds = 5
Debate termination limit; if increased, consensus rates might change.
sentence_limit = 4
Agents are limited to four sentences of reasoning; affects depth of analysis.

assumptions (4)

domain assumption The programmatically generated ambiguity labels are valid ground truth for ambiguity in user requests.
The generator defines what counts as ambiguous (Section 3.1); if real-world ambiguity differs, external validity fails.
ad hoc to paper A leader-follower debate with two followers and a majority agreement is an effective consensus mechanism.
No comparison with other debate topologies is provided; the protocol is introduced in Section 3.3.2 without justification beyond design choice.
domain assumption The LLM outputs can be reliably parsed into verdict and clarifying question.
Unrecoverable errors are mentioned in termination conditions, but the frequency is not reported; parsing failures would bias success rates.
domain assumption The three chosen LLMs are representative of small deployable models.
Results may not transfer to other small models; the paper generalizes from three architectures.

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Cite this review

Pith. "Pith review of Beyond Single Models: Enhancing LLM Detection of Ambiguity in Requests through Debate." pith.science (2026). https://pith.science/paper/DB5QXJSE

@misc{pith2026250712370,
  author       = {Pith},
  title        = {Pith review of: Beyond Single Models: Enhancing LLM Detection of Ambiguity in Requests through Debate},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DB5QXJSE}},
  note         = {Machine review of arXiv:2507.12370}
}

read the original abstract

Large Language Models (LLMs) have demonstrated significant capabilities in understanding and generating human language, contributing to more natural interactions with complex systems. However, they face challenges such as ambiguity in user requests processed by LLMs. To address these challenges, this paper introduces and evaluates a multi-agent debate framework designed to enhance detection and resolution capabilities beyond single models. The framework consists of three LLM architectures (Llama3-8B, Gemma2-9B, and Mistral-7B variants) and a dataset with diverse ambiguities. The debate framework markedly enhanced the performance of Llama3-8B and Mistral-7B variants over their individual baselines, with Mistral-7B-led debates achieving a notable 76.7% success rate and proving particularly effective for complex ambiguities and efficient consensus. While acknowledging varying model responses to collaborative strategies, these findings underscore the debate framework's value as a targeted method for augmenting LLM capabilities. This work offers important insights for developing more robust and adaptive language understanding systems by showing how structured debates can lead to improved clarity in interactive systems.

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Reference graph

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Human-Robot collaboration in surgery: Advances and challenges towards autonomous surgical assis- tants

J. Colan, A. Davila, Y . Yamada, and Y . Hasegawa, “Human-Robot collaboration in surgery: Advances and challenges towards autonomous surgical assis- tants”, Proceedings of the IEEE International Con- ference on Robot and Human Interactive Communi- cation (ROMAN), 2025

work page 2025

[2] [2]

Do As I Can, Not As I Say: Grounding language in robotic affordances

M. Ahn, A. Brohan, N. Brown, Y . Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman, and A. Herzog, “Do As I Can, Not As I Say: Grounding language in robotic affordances”, arXiv:2204.01691, 2022

arXiv 2022

[3] [3]

Grounding Multimodal LLMs to Embodied Agents that Ask for Help with Rein- forcement Learning

K. Ramachandran, A. Gokul, K. M. Krishna, and V . N. Balasubramanian, “Grounding Multimodal LLMs to Embodied Agents that Ask for Help with Rein- forcement Learning”, arXiv:2504.00907, 2025

arXiv 2025

[4] [4]

V oice con- trol interface for surgical robot assistants

A. Davila, J. Colan, and Y . Hasegawa, “V oice con- trol interface for surgical robot assistants”, Pro- ceedings of the International Symposium on Micro- NanoMechatronics and Human Science (MHS) , pp. 1–5, 2024

work page 2024

[5] [5]

Ambiguities in Spatial Language Understanding in Situated Human Robot Dialogue

C. Liu, J. Walker, and J. Y . Chai, “Ambiguities in Spatial Language Understanding in Situated Human Robot Dialogue”, Dialog with Robots: Papers from the AAAI Fall Symposium (FS-10-05) , pp. 50–55, 2010

work page 2010

[6] [6]

Demp- ster–Shafer theoretic resolution of referential am- biguity in situated human–robot dialogue

T. Williams, S. Schreitter, and M. Scheutz, “Demp- ster–Shafer theoretic resolution of referential am- biguity in situated human–robot dialogue”, Au- tonomous Robots , V ol. 43, No. 7, pp. 1811–1833, 2019

work page 2019

[7] [7]

Query Generation for Resolving Ambiguity in User’s Command for a Mo- bile Service Robot

K. Morohashi, and J. Miura, “Query Generation for Resolving Ambiguity in User’s Command for a Mo- bile Service Robot”, Proceedings of the European Conference on Mobile Robots (ECMR) , 2019

work page 2019

[8] [8]

Robotic Task Ambiguity Resolution via Natural Language Interaction

E. Chisari, J. O. von Hartz, F. Despinoy, and A. Val- ada, “Robotic Task Ambiguity Resolution via Natu- ral Language Interaction”, arXiv:2504.17748, 2025

work page Pith review arXiv 2025