REFERENCES
1. Du, Y.; Li, S.; Torralba, A.; Tenenbaum, J. B.; Mordatch, I. Improving factuality and reasoning in language models through multiagent debate. In Proceedings of the 41st International Conference on Machine Learning; PMLR, 2024; Vol. 235, pp 11733-63. Available online: https://proceedings.mlr.press/v235/du24e.html. (accessed 14 October 2026).
2. Madaan, A.; Tandon, N.; Gupta, P.; et al. Self-refine: iterative refinement with self-feedback. In Advances in Neural Information Processing Systems 36, New Orleans, Louisiana, USA, December 10-16, 2023; Neural Information Processing Systems Foundation, Inc. (NeurIPS), 2023, pp 46534-94.
3. Yao, S.; Zhao, J.; Yu, D.; et al. ReAct: synergizing reasoning and acting in language models. In International Conference on Learning Representations, 2023. Available online: https://openreview.net/forum?id=WE_vluYUL-X. (accessed 14 October 2026).
4. Asch, S. E. Effects of group pressure upon the modification and distortion of judgments. In Groups, Leadership and Men: Research in Human Relations; Guetzkow H., Ed.; Carnegie Press: Pittsburgh, PA, USA, 1951; pp 177-90. Available online: https://ndlsearch.ndl.go.jp/en/books/R100000136-I1574231874449715712. (accessed 14 October 2026).
5. Deutsch, M.; Gerard, H. B. A study of normative and informational social influences upon individual judgment. J. Abnorm. Soc. Psychol. 1955, 51, 629-36.
6. Cialdini, R. B.; Goldstein, N. J. Social influence: compliance and conformity. Annu. Rev. Psychol. 2004, 55, 591-621.
7. Zhu, X.; Zhang, C.; Stafford, T.; Collier, N.; Vlachos, A. Conformity in large language models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vienna, Austria, July, 2025; Association for Computational Linguistics, 2025, pp 3854-72.
8. Weng, Z.; Chen, G.; Wang, W. Do as we do, not as you think: the conformity of large language models. In International Conference on Learning Representations, 2025. Available online: https://openreview.net/forum?id=st77ShxP1K. (accessed 14 October 2026).
9. Ko, C.; Shin, J.; Song, H.; Lee, H.; Hwang, E. J.; Park, J. C. Social dynamics as critical vulnerabilities that undermine objective decision-making in LLM collectives. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), San Diego, California, United States, July, 2026; Association for Computational Linguistics, 2026, pp 37865-90.
10. Sharma, M.; Tong, M.; Korbak, T.; et al. Towards understanding sycophancy in language models. In International Conference on Learning Representations, 2024. Available online: https://openreview.net/forum?id=tvhaxkMKAn. (accessed 14 October 2026).
11. Choi, H. K.; Zhu, J.; Li, S. When identity skews debate: anonymization for bias-reduced multi-agent reasoning. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), San Diego, California, United States, July, 2026; Association for Computational Linguistics, 2026, pp 14284-311.
12. Sun, X.; Wu, D.; Qin, S.; Echizen, I.; El Ali, A.; Sugawara, S. Label effects: shared heuristic reliance in trust assessment by humans and LLM-as-a-judge. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), San Diego, California, United States, July, 2026; Association for Computational Linguistics, 2026, pp 32378-92.
13. Talmor, A.; Herzig, J.; Lourie, N.; Berant, J. CommonsenseQA: a question answering challenge targeting commonsense knowledge. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, June, 2019; Association for Computational Linguistics, 2019, pp 4149-58.
14. Hendrycks, D.; Burns, C.; Basart, S.; et al. Measuring massive multitask language understanding. In International Conference on Learning Representations, 2021. Available online: https://openreview.net/forum?id=d7KBjmI3GmQ. (accessed 14 October 2026).
15. Yang, A.; Yang, B.; Zhang, B.; et al. Qwen2.5 technical report. arXiv 2024, arXiv:2412.15115. Available online: https://doi.org/10.48550/arXiv.2412.15115 (accessed 14 October 2026).
16. Microsoft. Phi-3.5-mini-instruct model card. Hugging Face. Available online: https://huggingface.co/microsoft/Phi-3.5-mini-instruct. (accessed 14 October 2026).
17. Riviere, M.; Pathak, S.; Sessa, P. G.; et al. Gemma 2: improving open language models at a practical size. arXiv 2024, arXiv:2408.00118. Available online: https://doi.org/10.48550/arXiv.2408.00118 (accessed 14 October 2026).
18. Efron, B.; Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statist. Sci. 1986, 1, 54-75.
19. McNemar, Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 1947, 12, 153-7.
20. Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 1979, 6, 65-70. Available online: https://www.jstor.org/stable/4615733 (accessed 14 October 2026).
21. Li, G.; Hammoud, H.; Itani, H.; Khizbullin, D.; Ghanem, B. CAMEL: communicative agents for “Mind” exploration of large language model society. In Advances in Neural Information Processing Systems 36, New Orleans, Louisiana, USA, December 10-16, 2023; Neural Information Processing Systems Foundation, Inc. (NeurIPS), 2023, pp 51991-2008.
22. Wu, Q.; Bansal, G.; Zhang, J.; et al. AutoGen: enabling next-gen LLM applications via multi-agent conversations. In First Conference on Language Modeling, 2024. Available online: https://openreview.net/forum?id=BAakY1hNKS. (accessed 14 October 2026).
23. Mizrahi, M.; Kaplan, G.; Malkin, D.; Dror, R.; Shahaf, D.; Stanovsky, G. State of what art? A call for multi-prompt LLM evaluation. Trans. Assoc. Comput. Linguist. 2024, 12, 933-49.
