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Conformity in Large Language Models

Paper · 2025 · Xiaochen Zhu, Caiqi Zhang, Tom Stafford et al.

Summary

LLMs increasingly serve as conversational partners and as members of multi-agent ensembles. A tendency to side with a majority, even a wrong one, can therefore undermine their usefulness. The authors adapt the classic Asch conformity paradigm to LLMs. In a scripted dialogue, the model answers a question last, after p−1 "confederates" have all given the same distractor answer. They then measure how often the model switches to the distractor (conformity level) versus keeps its original answer (resistance level). For objective tasks, only questions the model first answered correctly are retained, which separates conformity from simple lack of knowledge. Experiments cover Llama-3, Qwen2, Gemma-2 and Mistral-v0.3 on objective and subjective datasets, varying training stage, confederate tone and agreement among confederates. Every model conformed to some degree in all domains. Lower initial confidence predicted more conformity, instruction-tuned models resisted better than base models, and more natural majority phrasing strengthened the effect. Two training-free prompting strategies reduce conformity, which supports building more reliable collaborative systems.

Key points

  • Benchmarks: MMLU, BigBenchHard (e.g., Object Counting), PopQA and CommonsenseQA for objective questions; Politiscale and OpinionsQA for subjective ones. The number of dialogue participants ranges from 2 to 10.
  • Confederate conditions are Unanimous or Diverse. In the Diverse control, answers are drawn at random, so there is no majority to follow. Tones are Plain (the base condition), Neutral, Confident and Uncertain.
  • The authors say no prior work has demonstrated what they find: a model that is more confident in its initial prediction is less prone to switching to an incorrect majority answer (p < 0.001).
  • Instruction-tuned variants are less susceptible than pre-trained base models. Conversational, natural-sounding majority responses increase conformity.
  • Mitigations:
    • Devil's Advocate: adds a dissenting wrong answer that breaks unanimity, inspired by Janis (1972). It also transfers to sycophancy mitigation.
    • Question Distillation: replaces the repeated majority answers with a short summary.
    • Code is released at github.com/SpaceHunterInf/llm_conformity.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of CambridgeCambridge · United Kingdom243 works · 121,396 citations🎓University of SheffieldSheffield · United Kingdom113 works · 21,542 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPhysics and Astronomy · 44 worksStatistical and Nonlinear Physics · 40 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Xiaochen Zhu, Caiqi Zhang, Tom Stafford, Nigel Collier and Andreas Vlachos. 2025. Conformity in Large Language Models. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.acl-long.195

@inproceedings{zhu2025conformity,
  title = {{Conformity in Large Language Models}},
  author = {Xiaochen Zhu, Caiqi Zhang, Tom Stafford, Nigel Collier and Andreas Vlachos},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.195},
  url = {https://doi.org/10.18653/v1/2025.acl-long.195}
}

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