Pith. sign in

REVIEW 3 major objections 4 minor 1 cited by

Multiple LLM Agents Debate for Equitable Cultural Alignment

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Putting two LLMs in a structured debate improves both accuracy and cultural parity on a 75-country social-norm test, letting 7-9B models match a 27B judge.

desk verdict A solid empirical study of multi-agent debate for cultural alignment; the judge-independent accuracy gains are convincing, but the parity and 'matches 27B' claims rest on a judge selected using the test set. read the letter →

arxiv 2505.24671 v2 pith:AAXHJQK5 submitted 2025-05-30 cs.CL cs.AI

Dayeon Ki , Rachel Rudinger , Tianyi Zhou , Marine Carpuat This is my paper
classification cs.CLcs.AI
keywords multi-agentdebateculturalalignmentsocialnormslargelanguagemodelsparityself-reflectionNormAd-ETI
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 tries to show that cultural alignment is not a single-model problem. It proposes a two-agent debate setup where different open-weight LLMs argue over a social-etiquette story and a judge LLM settles disagreements. Across 21 model pairs on the NormAd-ETI benchmark covering 75 countries, debate raises average accuracy over single-model baselines and improves parity between cultural groups. The sharpest claim is that 7-9B models in debate can match or exceed the single-model accuracy of a 27B judge, suggesting that collaboration can substitute for raw scale in cultural judgment.

What carries the argument

The central object is the multi-agent debate protocol: two LLM agents independently make an initial ternary decision on a country-tagged etiquette story, exchange written feedback, then produce a final decision; if they disagree, a separate judge LLM reads the debate transcript and gives the final answer. The two proposed variants, Debate-Only and Self-Reflect+Debate, both use this exchange-then-adjudicate loop, and the key mechanism is the complementary knowledge that different models bring to the same cultural scenario.

What would settle it

Re-run the evaluation with a judge model selected using only a held-out development split of the benchmark, with no access to NormAd-ETI test labels, then rerun the 21 model pairs; if 7-9B debate accuracy no longer reaches the 27B single-model level or cultural parity premiums fall toward single-model levels, the judge-selection shortcut is the load-bearing part.

Watch

Extended reading notes

Core claim

The paper's central discovery is that cultural adaptability improves more from having two different models debate than from making either model reflect alone. On NormAd-ETI, the Debate-Only variant reaches 76.3% average adjudicated accuracy and the Self-Reflect+Debate variant reaches 75.6%, versus 66.4% and 67.5% for the two single-model baselines. The best 7-9B pairs match the 79.2% single-model accuracy of the 27B judge, and debate also raises average cultural-group parity from 0.960 to 0.972. The authors read this as evidence that complementary training distributions and language coverage can be harvested through structured interaction, yielding both higher accuracy and more equitable alignment.

Load-bearing premise

The load-bearing premise is that the judge model's strength, which was measured on the same test benchmark, is not what drives the debate gains; if the judge were selected without access to test-set answers, the claimed parity and accuracy improvements might weaken.

Editorial extensions

If this is right

  • Debate-Only improves individual debater accuracy over the same model's single-turn baseline in 19 of 21 model pairs, and the adjudicated debate result beats single-model baselines in 20 of 21 pairs.
  • Two 7-9B models in debate can match the single-model accuracy of a 27B judge (79.2%): the best Debate-Only pair reaches 79.7% and the best Self-Reflect+Debate pair reaches 80.4%.
  • Debate shifts average cultural-group parity from 0.960 (single model with rule-of-thumb) to 0.972 (Debate-Only), with the best pairs reaching 0.994 and 0.986, bringing underrepresented cultural groups closer to English-speaking accuracy.
  • Decision dynamics show that the two agents usually start with mixed initial decisions (about 56% of cases), and the debate process converts many of those into correct joint final decisions, with the judge resolving most remaining disagreements correctly.
  • Adding self-reflection to debate does not systematically beat debate alone in final accuracy on average (75.6% versus 76.3%), but it does produce the single highest-performing model pair.

Reading between the lines

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

  • Editorial inference: The accuracy gains may be partly an ensemble effect, so a blind ensemble or routed combination of the same two 7-9B models might reproduce some of the improvement without any debate transcript or judge.
  • Editorial inference: Because parity is measured as accuracy relative to the best-performing cultural group, a natural testable extension is to compute worst-group accuracy, the more common fairness metric, to see whether debate also narrows the worst-group gap or mainly lifts already-strong groups.
  • Editorial inference: The 'small models beat a bigger model' framing depends on the judge being the strongest single model on the same benchmark, so a fairer comparison would select the judge on a held-out development split and then measure whether debate still closes the scale gap.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper proposes a Multi-Agent Debate framework for cultural alignment, with two variants: Debate-Only, where two LLM agents exchange feedback and a judge LLM resolves disagreements, and Self-Reflect+Debate, where each agent chooses between self-reflection and debate. The methods are evaluated on the NormAd-ETI benchmark with seven open-weight 7-9B LLMs and 21 LLM pairs, using GEMMA-2-27B as the judge. The authors report that individual debate accuracies exceed single-model baselines in 19/21 settings without the judge, that adjudicated debate accuracies exceed single-model baselines in 20/21 settings, that debate improves cultural group parity relative to English-speaking as the highest-accuracy group, and that 7-9B models reach accuracies comparable to the 27B judge. The paper includes detailed appendices with prompts, per-country results, decision dynamics, computational costs, and case studies, and it releases code and dataset.

Significance. If the headline claims hold, the paper offers a practical inference-time method for cultural alignment that exploits complementarity among open-weight LLMs without fine-tuning, and it introduces a cultural group parity metric that is directly relevant to fairness evaluations of LLMs. The most robust evidence is the judge-free result in §5.3 that individual debate accuracies D(M_i) exceed single-model baselines in 19/21 configurations, together with the oracle complementarity analysis in §5.2 and the transparent release of prompts, per-country tables, and code. However, the adjudicated accuracy numbers, the cultural parity claims in Table 3, and the 'comparable to 27B' claim in §5.5 all depend on a judge selected using the test set itself, and the parity analysis lacks uncertainty quantification. These issues are load-bearing for the paper's strongest conclusions and need to be addressed before the claims can be taken at face value.

major comments (3)
  1. [§4.2, Appendix B (Table 5), §5.3–§5.5, Table 3] The judge LLM GEMMA-2-27B was selected because it had the highest single-model accuracy on the NormAd-ETI test set among the candidates in Appendix B, Table 5. This makes the test set part of the model-selection procedure for the adjudicator. All adjudicated final accuracies D and S+D in Table 2, the parity premiums in Table 3, and the §5.5 claim that 7-9B models are comparable to a 27B model depend on this judge. A judge selected on a held-out development split, or an average over several judges chosen without using the test set, could yield smaller debate improvements and weaker parity gains. The individual debate accuracies D(M_i) in Table 2, which do not use the judge, are unaffected and provide the strongest support for the debate mechanism. I recommend re-running the adjudication with an unbiased judge-selection procedure and explicitly separating the judge-free evidence from the adjudicated evidence in the abstract, Section 5.5, and the conclusion.
  2. [§6.3, Table 3, Table 2 caption] The cultural group parity analysis reports point estimates only. The headline improvement in average parity is small (Si w/: 0.960 vs D: 0.972, and D vs judge J: 0.972 vs 0.964), and per-group premiums are computed on small samples; for example, the Confucian group contains 168 stories according to Table 19. No confidence intervals, bootstrap estimates, or significance tests are provided, and the blanket statement in the Table 2 caption that 'All improvements are statistically significant (p<0.05)' is not accompanied by a test procedure or by any multiple-comparison correction across 21 settings. Without such quantification, the claim that multi-agent debate 'yields more equitable cultural alignment' is not statistically established. Please add confidence intervals for parity premiums and report paired or group-wise tests for the accuracy comparisons.
  3. [§5.3, §5.4, Appendix C.3–C.4, Tables 7–8] The default configuration—one round of debate and one self-reflection iteration—was selected after running 1–5 rounds and N=1–3 reflection iterations on the same test set and choosing the configuration that performed best or was most efficient. The comparisons between debate and self-reflection, and the statement that a single round of debate is sufficient, therefore reflect test-set tuning rather than a prespecified design. This is a mild but real form of selection bias. Please either select hyperparameters on a validation split, report results for all round counts, or explicitly frame the current numbers as reflecting test-set-tuned configurations.
minor comments (4)
  1. [§6.1, Figure 2] The percentages quoted in the text (29.5% → 63.7% → 76.3% for Debate-Only) do not align with the labels printed in Figure 2 as rendered, where 'Final Correctness' appears to show 76.3% and 'Judge Correctness' appears to show 63.7%; please relabel the figure so that the three stages are unambiguous.
  2. [Table 6] The SEALLM-3 row reports exactly the same accuracies as the OLMo-SFT row (38.9 and 49.0); please verify whether this is a copy error.
  3. [§4.2] Please state explicitly which sampling temperature is used for the debater agents, for the self-reflection choice, and for the judge LLM; the current sentence ('We set the default sampling temperature to 0.0, and employ 0.8 where multiple runs are required') is not sufficient to reproduce the debate runs.
  4. [§8] The limitations section is candid about dataset scope and computational cost, but it does not mention the test-set-based judge selection or the test-set-based hyperparameter tuning; a sentence acknowledging these evaluation-design choices would be appropriate.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the main debate-accuracy claims rest on judge-independent individual accuracies, and the judge-based adjudicated results are empirical outcomes rather than by-construction reductions.

full rationale

The paper's central claim that multi-agent debate improves accuracy over single-LLM baselines is supported by judge-independent evidence: Section 5.3 reports 'individual debate accuracies of both LLM agents in Debate-Only setup (D(Mi)) outperform the single model baselines (Si(Mi)) in 19 out of 21 settings'. The 'comparable to 27B' claim is also not a construction artifact: Section 5.5 reports an individual (judge-independent) debate accuracy of 79.6% for GEMMA-2, compared with the independently measured single-model accuracy of 79.2% for GEMMA-2-27B. The judge LLM is selected based on its own single-model accuracy on the same benchmark (Appendix B, Table 5), which is a test-set-selection concern rather than a circularity: there is no equation by which the final debate accuracy is defined to equal the judge's score, and the reported final accuracies range from 70.1% to 80.4%, differing from the judge's 79.2%. The judge mechanism cites Li et al. 2024d, which shares an author with this paper, but that citation is not load-bearing: Appendix D independently benchmarks the judge against oracle and random disagreement resolution. No self-definitional, fitted-input-as-prediction, or self-citation-chain step was found.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claims rest on empirical measurements, not on fitted parameters. The main hand-set choices are the judge LLM and the number of debate rounds/self-reflection iterations, both selected using the test benchmark, which can bias reported scores. No new theoretical entities are introduced.

free parameters (4)
  • Judge LLM = GEMMA-2-27B
    Chosen from four candidates as the highest single-model accuracy (79.2%) on the NormAd-ETI test set (Appendix B, Table 5). This choice affects all adjudicated debate results and parity scores.
  • Number of debate rounds = 1
    Selected after testing 1-5 rounds on one model pair (LLAMA-3 + GEMMA-2) using the test set (Appendix C.3). Single round gave the best final accuracy.
  • Self-reflection iterations = 1
    Default; N=2 and N=3 increased accuracy slightly on two pairs but were not adopted (Appendix C.4).
  • Sampling temperature = 0.0 (default), 0.8 (repeated runs)
    Multiple-choice choices in Self-Reflect+Debate repeated twice with order alternated; no confidence intervals or seed variance reported (Section 4.2, Appendix C.5).
assumptions (5)
  • domain assumption NormAd-ETI ground truth labels are valid measures of cultural alignment.
    The task and conclusions treat the benchmark labels as ground truth for whether an action is socially acceptable in a country (Section 4.1). If labels are noisy or not representative, the accuracy and parity numbers inherit that.
  • domain assumption The rule-of-thumb included in each prompt is the relevant norm and is accurate for the country.
    All prompts state the rule is 'always accurate' (Appendix A); incorrect or incomplete rules would skew the task toward rule-matching.
  • domain assumption The Inglehart-Welzel cultural map grouping of 75 countries into 8 groups is a valid way to measure parity.
    Parity premiums are computed relative to the English-speaking group (Section 6.3, Appendix Table 19). Different groupings could change parity conclusions.
  • ad hoc to paper A single round of debate with two agents and no overlapping judge is sufficient to realize the benefit.
    The paper sets the default to one round after testing 1-5 rounds on one pair (Appendix C.3); this design choice limits exploration of longer debates.
  • ad hoc to paper The judge LLM is excluded from the debater pool.
    A design decision to avoid overlap (Appendix B); using a different judge or a judge from the debater family might change results.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Multiple LLM Agents Debate for Equitable Cultural Alignment." pith.science (2026). https://pith.science/paper/AAXHJQK5

@misc{pith2026250524671,
  author       = {Pith},
  title        = {Pith review of: Multiple LLM Agents Debate for Equitable Cultural Alignment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AAXHJQK5}},
  note         = {Machine review of arXiv:2505.24671}
}
read the original abstract

Large Language Models (LLMs) need to adapt their predictions to diverse cultural contexts to benefit diverse communities across the world. While previous efforts have focused on single-LLM, single-turn approaches, we propose to exploit the complementary strengths of multiple LLMs to promote cultural adaptability. We introduce a Multi-Agent Debate framework, where two LLM-based agents debate over a cultural scenario and collaboratively reach a final decision. We propose two variants: one where either LLM agents exclusively debate and another where they dynamically choose between self-reflection and debate during their turns. We evaluate these approaches on 7 open-weight LLMs (and 21 LLM combinations) using the NormAd-ETI benchmark for social etiquette norms in 75 countries. Experiments show that debate improves both overall accuracy and cultural group parity over single-LLM baselines. Notably, multi-agent debate enables relatively small LLMs (7-9B) to achieve accuracies comparable to that of a much larger model (27B parameters).

Figures

Figures reproduced from arXiv: 2505.24671 by the authors.

Figure 1
Figure 1. Given a country, rule-of-thumb, and story from the NORMAD-ETI dataset, each method predicts a ternary label (Yes, No, Neither). Single-LLM: 1) Single Model: A single LLM predicts the label, with or without rule-of-thumb information. 2) Self-Reflection: LLM reflects on its initial outputs and revises its prediction. Multi-LLM Collaboration: 3) Debate-Only: Two LLM-based agents engage in a debate, generating initial d… view at source ↗
Figure 2
Figure 2. How model decisions evolve through (a) Self-Reflection, (b) Debate-Only, and (c) Self-Reflect+Debate, each aggregated across all LLMs or LLM combinations. 1) Initial Correctness: whether the model’s initial decision is correct; 2) Final Correctness: whether the individual model’s final decision is correct; 3) Judge Correctness: whether the judge LLM’s debate adjudication decision is correct. If both models evaluated… view at source ↗
Figure 3
Figure 3. Decision dynamics per ground truth label group ∈ {Yes, No, Neither} for Self-Reflection baseline. 1) Initial Correctness: whether the LLM makes correct initial decision; 2) Final Correctness: whether the LLM makes correct final decision. Cultural group Initial ( ) Initial ( ) Initial ( ) Final ( ) Final ( ) Final ( ) Judge ( ) Judge ( ) African-Islamic 5426 2632 10674 11750 3374 3608 14131 4601 Catholic Europe 1999 … view at source ↗
Figures from the paper (15 more)
Figure 4
Figure 4. Figure 4: Decision dynamics per ground truth label group ∈ {Yes, No, Neither} for Debate-Only baseline. 1) Initial Correctness: whether both of the LLMs make correct initial decisions; 2) Final Correctness: whether both of the LLMs make correct final decisions; 3) Judge Correctn…
Figure 5
Figure 5. Figure 5: Decision dynamics per ground truth label group ∈ {Yes, No, Neither} for Self-Reflect+Debate baseline. 1) Initial Correctness: whether both of the LLMs make correct initial decisions; 2) Final Correctness: whether both of the LLMs make correct final decisions; 3) Judge …
Figure 6
Figure 6. Figure 6: Decision dynamics per ground truth label group ∈ {Yes, No, Neither} for Self-Reflection baseline. Initial Correctness Final Correctness Judge Correctness 0 5000 10000 15000 20000 25000 30000 35000 40000 Count 54.1% 49.4% 44.6% 1.2% 5.8% 9.0% 22.0% 31.0% 45.8% 44.0% 38.…
Figure 8
Figure 8. Figure 8: Decision dynamics per ground truth label group ∈ {Yes, No, Neither} for Self-Reflect+Debate baseline [PITH_FULL_IMAGE:figures/full_fig_p030_8.png]
Figure 7
Figure 7. Figure 7: Decision dynamics per ground truth label group ∈ {Yes, No, Neither} for Debate-Only baseline. Note that “Cor￾rect” and “Incorrect” denotes both models are correct/incorrect whereas “Mixed” denotes one correct and one incorrect. We show that debate specifically improves…
Figure 9
Figure 9. Figure 9: Mean accuracies of each method, categorized by Inglehart-Welzel cultural groups. The accuracies for each group follow a consistent improvement pattern: Single Model without rule-of-thumb < Single Model with rule-of-thumb < Self￾Reflection < Self-Reflect+Debate < Debate…
Figure 10
Figure 10. Figure 10: Case study example of a debate with correct initial and final decisions. (1): Initial Correctness; (2): Final Correctness; (3): Judge Correctness. If the final decisions of the two debater agents are identical, we do not use the judge LLM. We use the same scheme for a…
Figure 11
Figure 11. Figure 11: Case study example of a debate with incorrect initial and correct final decisions [PITH_FULL_IMAGE:figures/full_fig_p033_11.png]
Figure 12
Figure 12. Figure 12: Case study example of a debate with mixed initial and correct final decisions. Case Study. (1) Correct → (2) Mixed → (3) Correct Country: Argentina Rule-of-thumb: It is respectful to excuse oneself when needing to pass through a conversation. Story: During a gathering…
Figure 13
Figure 13. Figure 13: Case study example of a debate with correct initial, mixed final and correct judge LLM decision [PITH_FULL_IMAGE:figures/full_fig_p034_13.png]
Figure 14
Figure 14. Figure 14: Case study example of a debate with incorrect initial, mixed final and correct judge LLM decision. Case Study. (1) Mixed → (2) Mixed → (3) Correct Country: Ireland Rule-of-thumb: It is correct to say ‘bye’ multiple times before ending a phone call. Story: Sarah was in…
Figure 15
Figure 15. Figure 15: Case study example of a debate with mixed initial, mixed final and correct judge LLM decision [PITH_FULL_IMAGE:figures/full_fig_p035_15.png]
Figure 16
Figure 16. Figure 16: Case study example of a debate with correct initial and incorrect final decisions. Case Study. (1) Incorrect → (2) Incorrect Country: South Sudan Rule-of-thumb: It is okay to bring additional friends to a dinner invitation, as hosts usually prepare more food than need…
Figure 17
Figure 17. Figure 17: Case study example of a debate with incorrect initial and final decisions [PITH_FULL_IMAGE:figures/full_fig_p036_17.png]
Figure 18
Figure 18. Figure 18: Case study example of a debate with mixed initial and incorrect final decisions [PITH_FULL_IMAGE:figures/full_fig_p037_18.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives

    cs.AI 2025-06 conditional novelty 4.0 of 10

    A survey proposes a macro-meso-micro value framework for agentic AI alignment and maps applications, methods, and benchmarks onto it.

Reference graph

Works this paper leans on

18 extracted references · 9 canonical work pages · cited by 1 Pith paper

  1. [1]

    AI, :, Alex Young, Bei Chen, Chao Li, Chen- gen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, Kaidong Yu, Peng Liu, Qiang Liu, Shawn Yue, Senbin Yang, Shiming Yang, Tao Yu, Wen Xie, Wenhao Huang, Xiaohui Hu, Xiaoyi Ren, Xinyao Niu, Pengcheng Nie, Yuchi Xu, Yudong Liu, Yue Wang, Yuxuan Cai, Zhenyu Gu, Zhiyuan Liu, and Z...

    arXiv 2024

  2. [2]

    While Sarah’s action of quickly ending the call without waiting for a response could be seen as impolite or rushed, it’s not entirely clear from the story whether this was appropriate or not

    work page

  3. [3]

    The story doesn’t provide enough context about the nature of the call, the relationship between Sarah and Tom, or the urgency of the situation

    work page

  4. [4]

    Preprint, arXiv:2407.02273

    Language model alignment in multilingual trolley problems. Preprint, arXiv:2407.02273. Yuu Jinnai. 2024. Does cross-cultural alignment change the commonsense morality of language models? In Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP, pages 48–64, Bangkok, Thai- land. Association for Computational Linguistics. Saurav Kadavath, ...

    arXiv 2024

  5. [5]

    In Proceedings of the 62nd Annual Meeting of the Association for Compu- tational Linguistics (V olume 1: Long Papers), pages 16366–16393, Bangkok, Thailand

    Having beer after prayer? measuring cultural bias in large language models. In Proceedings of the 62nd Annual Meeting of the Association for Compu- tational Linguistics (V olume 1: Long Papers), pages 16366–16393, Bangkok, Thailand. Association for Computational Linguistics. OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Flo...

    arXiv 2024

  6. [6]

    Preprint, arXiv:2305.18290

    Direct preference optimization: Your lan- guage model is secretly a reward model. Preprint, arXiv:2305.18290. Krithika Ramesh, Sunayana Sitaram, and Monojit Choudhury. 2023. Fairness in language models be- yond English: Gaps and challenges. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2106–2119, Dubrovnik, Croatia. Associ...

    arXiv 2023

  7. [7]

    Understanding the capabilities and limitations of large language models for cultural commonsense. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies (V olume 1: Long Papers), pages 5668–5680, Mexico City, Mexico. Association for Computational Lin- guistics. We...

    work page 2024

  8. [9]

    In Proceedings of the 62nd Annual Meeting of the As- sociation for Computational Linguistics (V olume 1: Long Papers), pages 6349–6384, Bangkok, Thailand

    Not all countries celebrate thanksgiving: On the cultural dominance in large language models. In Proceedings of the 62nd Annual Meeting of the As- sociation for Computational Linguistics (V olume 1: Long Papers), pages 6349–6384, Bangkok, Thailand. Association for Computational Linguistics. Shaoyang Xu, Yongqi Leng, Linhao Yu, and Deyi Xiong

    work page

Show all 18 references
  1. [10]

    Preprint, arXiv:2410.12971

    Self-pluralising culture alignment for large language models. Preprint, arXiv:2410.12971. Jifan Yu, Xiaozhi Wang, Shangqing Tu, Shulin Cao, Daniel Zhang-Li, Xin Lv, Hao Peng, Zijun Yao, Xiao- han Zhang, Hanming Li, et al. 2023. Kola: Carefully benchmarking world knowledge of l...

    arXiv

  2. [11]

    In Findings of the Association for Computational Lin- guistics: NAACL 2024 , pages 3104–3117, Mexico City, Mexico

    RENOVI: A benchmark towards remediating norm violations in socio-cultural conversations. In Findings of the Association for Computational Lin- guistics: NAACL 2024 , pages 3104–3117, Mexico City, Mexico. Association for Computational Lin- guistics. Wenxuan Zhang, Hou Pong Chan...

    2024 arXiv

  3. [14]

    The story does not align with the specific behavior of saying ‘bye’ multiple times before ending a phone call, which is the rule given

  4. [17]

    In some professional contexts, ending a call quickly might be more appropriate than prolonging it unnecessarily

  5. [18]

    Yes” ( conformations) and worst at “Neither

    We find that the best-performing model varies across countries, which motivates us to explore a multi-LLM setup (§5.2). 10https://openai.com/index/chatgpt/ Model Training Method Si (w/o) Si (w/) LLAMA-3 SFT + RLHF 49.5 63.7 GEMMA-2 SFT + RLHF 50.7 68.9 EXAONE-3 SFT + DPO 42.8 ...

    2023

  6. [19]

    Neither

    The story doesn’t mention any cultural or social norms specific to Ireland that would make Sarah’s action particularly acceptable or unacceptable. Given these points, the story is neither clearly relevant nor clearly irrelevant to the rule. Therefore, the most appropriate answ...

  7. [20]

    Cor- rect

    For the aggregated results across all LLMs or LLM combinations, the average parity fol- lows the order of Single Model without rule-of- thumb < Single Model with rule-of-thumb < Self- Reflect+Debate < Self-Reflection < Debate-Only. For specific LLMs, the average parity ranks a...

  8. [2018]

    arXiv preprint arXiv:1805.00899

    Ai safety via debate. arXiv preprint arXiv:1805.00899. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Men- sch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guil- laume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne La...

    2023 arXiv

  9. [2023]

    Preprint, arXiv:2303.11366

    Reflexion: Language agents with verbal rein- forcement learning. Preprint, arXiv:2303.11366. Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupati- raju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, Johan Ferret, P...

    2024 arXiv

  10. [2024]

    In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (V ol- ume 1: Long Papers) , pages 15789–15809, Bangkok, Thailand

    OLMo: Accelerating the science of language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (V ol- ume 1: Long Papers) , pages 15789–15809, Bangkok, Thailand. Association for Computational Linguistics. Shirley Anugrah Hayati, M...

    2024 arXiv

Pith tools

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