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REVIEW 3 major objections 6 minor 58 references

Arbiters of Ambivalence: Challenges of Using LLMs in No-Consensus Tasks

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read LLMs that answer neutrally take stances when asked to judge or debate the same questions.

desk verdict Useful benchmark and a real judge/debate stance-taking effect, but the abstract oversells answer-generator neutrality by implying open-ended generation when the prompts explicitly offered a neutral option. read the letter →

arxiv 2505.23820 v1 pith:LVZTPSV7 submitted 2025-05-28 cs.CL

Bhaktipriya Radharapu , Manon Revel , Megan Ung , Sebastian Ruder , Adina Williams This is my paper
classification cs.CL
keywords LLM-as-a-judgeno-consensusbenchmarkneutralitypreferencealignmentscalableoversightdebatehumandisagreementambivalence
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

This paper asks whether large language models (LLMs) can stay neutral on questions where humans themselves disagree, when the same model is used as an answer generator, a judge, or a debater. To test this, the authors assemble a no-consensus benchmark of ten datasets spanning moral dilemmas, causal ambiguity, subjective taste, contested politics, cultural norms, and known unknowns, and measure how often five models return a neutral answer in each role. The central finding is that models are often neutral when generating answers directly, but become markedly more likely to take a stance when asked to judge or debate the same material. Because LLM judges and debaters are increasingly used to replace humans in preference alignment and scalable oversight, the paper's claim is that those pipelines may systematically erase the very disagreement they are meant to represent.

What carries the argument

The load-bearing object is the No-Consensus Benchmark: ten datasets across seven categories of ambivalence, each item a binary question with two valid stances, filtered where possible by high human label entropy so that the most frequent answer wins no more than roughly 70 percent of human votes. The carrying metric is neutrality: the share of answer-generator responses marked "Both/Neutral/Debatable," the share of pairwise or debate judgments called a tie, or the share of pointwise judgings where both stances receive equal scores. The central comparison is the difference between answer-generator neutrality and judge or debate neutrality, and the central prompt device is the explicit neutral option in the answer-generator condition, which the paper introduces to maximize the models' opportunity to stay neutral.

What would settle it

Run the full benchmark on all five models in answer-generator mode without any explicit neutral option, then compare the neutrality rates with the same models' pairwise-judge and debate rates; if the mean difference falls to near zero, the claim that neutrality fails to carry over into judging and debating collapses.

Watch

Extended reading notes

Core claim

The paper's central discovery is that LLM neutrality is role-dependent: on a curated benchmark of questions where humans split roughly evenly between two stances, all five tested models return neutral or "both" answers considerably more often when asked directly to answer than when asked to rank, score, or adjudicate between the same two stances. The authors report large negative differences between answer-generator neutrality and pairwise-judge neutrality across nearly all model-task combinations, smaller but still substantial drops in pointwise judging and debate, and a debate-judge ablation showing that a judge's neutrality is nearly independent of which models produced the debate transcripts. They interpret this as evidence that current alignment methods create a "facade of neutrality" that appears in direct generation but does not survive translation into judge or debate frameworks.

Load-bearing premise

The high neutrality attributed to answer generators comes from prompts that explicitly offer a neutral or "both" option; if the model were simply asked open-ended, its neutrality drops sharply, which would shrink the gap this paper's central contrast depends on.

Editorial extensions

If this is right

  • Preference-alignment pipelines that use LLM judges to rate or rank candidate responses will tend to convert no-consensus inputs into one-sided verdicts, hiding the underlying human disagreement.
  • Debate-based scalable oversight, at least under the one-by-one protocol used here, cannot be relied on to preserve neutrality because the judge's stance appears fixed before reading the debate transcripts.
  • Benchmarks for LLM judges should include no-consensus items alongside tasks with correct answers, since high agreement with human majorities on math or writing does not predict behavior where humans split.
  • The apparent neutrality of open-ended answer generation is partly a function of prompting: without an explicit neutral option, even GPT-4o's neutrality drops on most tasks in the benchmark.
  • Models can be steered to argue both sides well, with score differences averaging about one point, so the limitation is not in generating balanced arguments but in adjudicating between them neutrally.

Reading between the lines

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

  • If the answer-generator baseline were run without the explicit neutral option the paper adds in Section 4.1, the paper's own Appendix C.1 suggests GPT-4o's neutrality would drop substantially; extending that ablation to all five models could shrink or erase the reported gap between answer generators and judges.
  • The role effect may partly be a prompt effect: answer generation gets a "Both/Debatable" escape hatch while judge prompts ask for a winner or a tie, so the two modes may not be measuring the same disposition without matched prompts.
  • A practical extension would be to prompt judges to output calibrated disagreement probabilities, such as "60% of humans choose Yes," and check whether those estimates track the human label distributions in the benchmark.
  • If the debate judge's stance is indeed fixed before reading transcripts, debate-based oversight cannot be expected to make adjudication fairer; one would instead need to select judges by baseline neutrality or train them to update on evidence.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. This paper constructs a 'no-consensus' benchmark from 10 existing datasets across 7 categories and evaluates five LLMs in three roles: answer generation, pairwise/pointwise judging, and debate. The central empirical claim is that LLMs give neutral/nuanced answers as direct answer generators but take stances when acting as judges or debaters, and that debate judges' neutrality is largely determined by the judge model rather than the debate transcript. The paper also reports JSD comparisons between LLM judge labels and human label distributions.

Significance. The paper addresses an important and timely problem: whether LLM-as-judge and debate-based scalable oversight preserve the apparent neutrality of generative models on inherently disputed questions. Its strengths are the multi-dataset curation with human disagreement filtering, the three-mode design, the explicit ablation of open-ended vs constrained generation (Appendix C.1), and the candid discussion of normative assumptions in the Limitations section. If the central contrast survived a cleaner prompt-controlled comparison, the finding would be of clear value to alignment and evaluation practice.

major comments (3)
  1. [§4.1, Abstract, and Appendix C.1/Table 5] The headline contrast is confounded by a prompt-design difference. The abstract and introduction describe the answer-generator condition as 'open-ended answers,' but the main answer-generator prompts in §4.1 and Appendix D.1.2 explicitly instruct the model to end with [[Yes]], [[No]], and [[Both]]/[[Neutral]]/[[Debatable]]. Appendix C.1 Table 5 shows that without such an explicit neutral option, GPT-4o's neutrality drops substantially (e.g., ConflictingQA from 85% to 31%, Scruples Anecdote from 73% to 21%, Moca Moral from 83.7% to 30.2%, KUQ from 79.8% to 31.0%). Since Figure 3 compares judge/debate neutrality against this constrained answer-generator baseline, part of the reported gap is an artifact of making the neutral option more salient in the generator condition. The manuscript should either re-run or re-report the main comparison with open-ended generation, or restrict the claim to 'constrained answer generators.'
  2. [§4.2, §4.3, Appendices D.2 and D.3] The judge and debate conditions also include a tie/neutral option ([[C]] in the pairwise prompt, 'Tie' in the debate judge, and equal-score ties in pointwise judging), so the mode comparison does not isolate role-based stance-taking from the availability/salience of a neutral response. The paper's stated design choice was to 'explicitly allow the option of a neutral answer or tie across all modes,' which is sensible for maximizing neutrality, but it means the answer-generator condition differs in how strongly the neutral option is requested. A cleaner test would vary the prompt's neutral-option salience factorially across all three modes; without this, the conclusion that 'LLMs do not carry their answer-generator neutrality into judge or debate frameworks' is overstated.
  3. [§5.2 and Figure 4] The claim that debate judges 'maintain their initial stance... regardless of the LLM debaters involved' is supported only by descriptive percentages with no uncertainty or significance testing. The Appendix A.3 heatmaps show cells that differ by tens of percentage points across judge/debater pairs, so the conclusion that stance is 'inherent' needs confidence intervals or a statistical test, or should be softened to a tendency.
minor comments (6)
  1. [Abstract and Introduction] The abstract's 'open-ended answers' should be reconciled with the constrained prompt described in §4.1; this wording also appears in contribution (iii) in the Introduction.
  2. [Throughout] There are frequent typos: 'debators' should be 'debaters' (e.g., in the Introduction and Section 4.3), and Appendix D.1.2 lists 'Procon' twice among the datasets for the yes/no prompt.
  3. [Figures 1 and 3] Figures 1 and 3 would benefit from confidence intervals or error bars; the three-round averaging is mentioned but no variance is shown, and the sign convention for the Figure 3 heatmaps (negative = reduction in neutrality relative to answer generation) should be stated directly in the caption.
  4. [Section 8 (Limitations)] In the Limitations section, 'we have either argued for our own decision to count a task/dataset as "no disagreement"' appears to be a typo for 'no agreement.'
  5. [Table 5] Table 5 would be easier to read if the 'Open Ended Generation' column were labeled consistently and the difference column aligned; the table currently mixes percentages with two decimals and whole numbers.
  6. [Section 3.2, Subjective Topics] The hand-curated Subjective dataset is generated by Llama-3.1 405B without human validation; since no human label distribution is available for this dataset, the paper should state explicitly that neutrality here is measured relative to the model's own binary framing.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central answer-generator/judge contrast is a disclosed prompt-design choice, not an identity between fitted inputs and reported predictions.

full rationale

The paper's central measurement is a transparent, uniformly applied neutrality metric: answer-generator neutrality is the fraction of responses that return an explicitly offered neutral verdict (e.g., [[Both]], [[Neutral]], [[Debatable]]), while judge and debate neutrality are fractions of ties or equal scores. The benchmark is assembled from external datasets (Scruples, Moca, Alpha NLI, KUQ, Procon, ConflictingQA, Social Chemistry, Normad), and no parameter is fitted to the data and then renamed as a prediction. The most serious concern is that the main answer-generator prompt explicitly includes a neutral option, so the reported gap vs. judges partly reflects prompt compliance rather than role-based behavior. However, the paper itself discloses this: Section 4.1 explains that open-ended generation without the neutral option led models to take stances, and Appendix C.1 quantifies the drop for GPT-4o (e.g., ConflictingQA neutrality 85% to 31%). This is an acknowledged design choice and validity threat, not a logical circle in which an output is equivalent to its input by definition. Self-citations (e.g., Radharapu et al. 2023, Hao et al. 2023) appear only in related-work enumerations and do not carry the argument. The derivation is self-contained against external benchmarks, so the appropriate circularity score is 0.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The paper is an empirical benchmark study, so few free parameters are involved. The main modeling choices are the disagreement threshold (t=0.7), the number of debate rounds, and the sampling hyperparameters. The normative assumption that neutrality is desirable on these tasks is acknowledged in the limitations. No new entities (particles, forces, dimensions) are introduced.

free parameters (3)
  • consensus threshold t = 0.7
    Used to filter datasets for high-disagreement examples: only questions where the most frequent answer is chosen by at most 70% of annotators are kept. The value is hand-chosen to balance sample size and disagreement level; results could shift with a different threshold.
  • number of debate rounds = 3
    The debate protocol uses three rounds of arguments. This follows prior work (Khan et al., 2024), but neutrality outcomes might depend on round count.
  • temperature / top-p = 0.8 / 1.0
    Sampling hyperparameters used across all models. These affect response variability and thus neutrality measurements, though they are standard choices.
assumptions (3)
  • domain assumption No-consensus questions are adequately modeled as binary questions with two valid stances.
    The benchmark restricts to two-stance questions for tractability, but real disagreements often involve more than two positions. The paper acknowledges this in the Limitations.
  • domain assumption Neutrality is the appropriate model behavior on the 10 benchmark tasks.
    The authors argue that for their chosen tasks, a neutral or nuanced response is desirable, but they explicitly note in Limitations that not every no-consensus task should be handled neutrally (e.g., legal disagreements).
  • domain assumption Same-model debate and judging is representative of scalable oversight pipelines.
    The debate uses three instances of the same model as debaters and judge. Real systems may mix models of different sizes or families, which could change neutrality behavior.

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Cite this review

Pith. "Pith review of Arbiters of Ambivalence: Challenges of Using LLMs in No-Consensus Tasks." pith.science (2026). https://pith.science/paper/LVZTPSV7

@misc{pith2026250523820,
  author       = {Pith},
  title        = {Pith review of: Arbiters of Ambivalence: Challenges of Using LLMs in No-Consensus Tasks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LVZTPSV7}},
  note         = {Machine review of arXiv:2505.23820}
}
read the original abstract

The increasing use of LLMs as substitutes for humans in ``aligning'' LLMs has raised questions about their ability to replicate human judgments and preferences, especially in ambivalent scenarios where humans disagree. This study examines the biases and limitations of LLMs in three roles: answer generator, judge, and debater. These roles loosely correspond to previously described alignment frameworks: preference alignment (judge) and scalable oversight (debater), with the answer generator reflecting the typical setting with user interactions. We develop a ``no-consensus'' benchmark by curating examples that encompass a variety of a priori ambivalent scenarios, each presenting two possible stances. Our results show that while LLMs can provide nuanced assessments when generating open-ended answers, they tend to take a stance on no-consensus topics when employed as judges or debaters. These findings underscore the necessity for more sophisticated methods for aligning LLMs without human oversight, highlighting that LLMs cannot fully capture human disagreement even on topics where humans themselves are divided.

Figures

Figures reproduced from arXiv: 2505.23820 by the authors.

Figure 1
Figure 1. Percentage of Neutral Answers on the No￾Consensus Benchmark. We test whether LLMs are more or less likely to take a stance when provided with ambivalent scenarios as answer generator, pairwise or pointwise judge, and debater. When generating an an￾swer directly, LLMs are fairly neutral or nuanced. How￾ever, they are much more likely across the board to take a stance when acting as judge (either pairwise or point￾wis… view at source ↗
Figure 2
Figure 2. Evaluation of three common applications of LLMs: (i) as answer generators, where LLMs respond to input queries; (ii) as judges, where LLMs rank or score inputs in either pairwise (both stances are presented together) or pointwise (each stance is shown and scored individually) modes; and (iii) as debaters, where two LLMs engage in a multi-turn dialogue to debate an issue, with a third LLM reviewing the conversation a… view at source ↗
Figure 3
Figure 3. Neutrality Results: Average Difference in Scores between Answer Generator and the Pairwise Judge, the Pointwise Judge and the Debater. The heatmaps show the difference in neutrality scores between (left) pairwise judges, (center) pointwise judges and (right) debate and answer generators across different models. They reveal that LLM judges and LLM debaters have a strong bias towards their preferred stance regardless … view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Jensen-Shannon Divergence against en￾tropy LLM labels averaged across Causal Ambiguity, Linguistic Inference Ambiguity and Moral Ambiguity tasks for both judge modes. human distribution. Pairwise judges are generally more decisive (lower entropy) and less neutral than …
Figure 6
Figure 6. Figure 6: Jensen-Shannon Divergence between Human Annotations and LLM labels averaged across Causal Ambiguity, Linguistic Inference Ambiguity and Moral Ambiguity tasks for both judge modes [PITH_FULL_IMAGE:figures/full_fig_p018_6.png]
Figure 7
Figure 7. Figure 7: Model Impact on Debate Results The heatmaps show the percentage of neutrality when various LLMs are used as debaters (y axis) and as judges (x axis). We note that the model seems to have little impact on the outcome except that Llama-8B is consistently better at judgin…
Figure 8
Figure 8. Figure 8: Mean absolute difference between winning and losing stance as scored by pointwise judge across models. We see that difference in scores between the two stances is on an average under 2 points for all models for all tasks [PITH_FULL_IMAGE:figures/full_fig_p020_8.png]

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    Sustainable

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    ** Strengths and Weaknesses of Debater A ’ s Arguments :** - ** Strengths :** - Cites authoritative sources like the American Dietetic Association and the UN , lending credibility to health and environmental claims . - Provides specific health benefits ( lower risks of heart d...

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    - Points out the cultural and traditional significance of meat , which is a valid consideration in dietary choices

    ** Strengths and Weaknesses of Debater B ’ s Arguments :** - ** Strengths :** - Highlights the importance of essential nutrients like vitamin B12 , iron , and omega -3 s , which are more bioavailable in meat . - Points out the cultural and traditional significance of meat , wh...

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    Health benefits : Reduced risk of chronic diseases

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    Studies showing lower rates of heart disease , diabetes , and cancer in vegetarians

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    Argument planning : - Health benefits : Essential nutrients from meat ( e . g . , B12 , iron , omega -3) . - Environmental impact : Sustainable livestock farming practices . - Cultural significance : Meat in traditions and cuisines

  33. [46]

    - Improved livestock farming techniques reducing environmental damage

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  34. [47]

    - Overestimating the effectiveness of sustainable livestock farming

    Biggest flaws in opponent ’ s argument : - Claims about essential nutrients without addressing plant - based alternatives . - Overestimating the effectiveness of sustainable livestock farming . - Cultural and traditional values argument lacks universality

  35. [48]

    - Point out that sustainable livestock farming is not widespread and still poses significant environmental issues

    Step - by - step critique : - Highlight plant - based sources for essential nutrients like vitamin B12 ( fortified foods ) , iron ( legumes , spinach ) , and omega -3 fatty acids ( flaxseeds , chia seeds ) . - Point out that sustainable livestock farming is not widespread and ...

  36. [49]

    - Data showing the limited impact of sustainable livestock farming

    Best counterexamples / evidences : - Reliable sources for plant - based nutrients . - Data showing the limited impact of sustainable livestock farming . - Examples of cultures with vegetarian traditions adapting positively . </ thinking > < argument > My opponent suggests that...

  37. [50]

    - Environmental impact of livestock farming can be mitigated by sustainable practices

    Biggest flaws in opponent ’ s argument : - Health benefits overlook the need for certain nutrients primarily found in animal products . - Environmental impact of livestock farming can be mitigated by sustainable practices . - Ethical considerations are subjective and vary acro...

  38. [51]

    - Emphasize the improvements in sustainable farming practices and lower - impact meat alternatives

    Critique opponent ’ s argument : - Highlight the necessity of nutrients like vitamin B12 and omega -3 s , which are scarce in vegetarian diets . - Emphasize the improvements in sustainable farming practices and lower - impact meat alternatives . - Address the cultural , tradit...

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.