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Multiple LLM Agents Debate for Equitable Cultural Alignment

Paper · 2025 · Dayeon Ki, Rachel Rudinger, Tianyi Zhou et al.

Summary

Large language models handle culturally specific situations unevenly. Most earlier attempts to fix this use one model answering in a single pass. This paper asks whether several open-weight LLMs, trained on different data with different alignment, can cover each other's cultural blind spots by debating.

The authors propose a Multi-Agent Debate framework. Two agents each give a verdict on a social-etiquette scenario, critique each other's reasoning, and revise their answers. If they still disagree, a judge LLM decides. A second variant, Self-Reflect+Debate, lets each agent choose on every turn whether to reflect on its own answer or respond to its partner.

Both variants are compared with single-model prompting (with or without a rule-of-thumb) and with self-reflection. The testbed is the NormAd-ETI benchmark, using 7 models and all 21 pairings.

Debate improves overall accuracy and narrows performance gaps between cultural groups. It also lets 7–9B models reach the level of Gemma-2-27B. The work shows that inference-time collaboration between models is a practical route toward fairer cultural alignment.

Key points

  • NormAd-ETI contains about 2.6K stories covering etiquette norms from 75 countries, drawn from the Cultural Atlas. Each story has a ternary label: Yes, No or Neither.
  • Debate-Only improved individual accuracy in 20 of 21 model pairings, and Self-Reflect+Debate in 14 of 21.
  • Debating pairs of small (7–9B) models approached the accuracy of Gemma-2-27B (79.2%), which also served as the judge.
  • Debate gave the best cultural group parity, measured relative to the English-speaking group, with the largest gains for underrepresented groups. Countries in the African Islamic and Orthodox Europe groups remained the hardest.
  • Code and data are released at github.com/dayeonki/cultural_debate. Stated limitations include using a single dataset with 30–40 stories per country and the extra compute cost of debate.

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

 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 worksSocial Sciences · 1,772 worksSafety Research · 543 works

Cite

Cite as Dayeon Ki, Rachel Rudinger, Tianyi Zhou and Marine Carpuat. 2025. Multiple LLM Agents Debate for Equitable Cultural Alignment. 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.1210

@inproceedings{ki2025multiple,
  title = {{Multiple LLM Agents Debate for Equitable Cultural Alignment}},
  author = {Dayeon Ki, Rachel Rudinger, Tianyi Zhou and Marine Carpuat},
  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.1210},
  url = {https://doi.org/10.18653/v1/2025.acl-long.1210}
}

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