Perspectives on Cross-Lingual Consistency in LLMs for Medical Questions

2026-09-07

AI Summary

论文探讨了多语言大模型在回答医学问题时应保持一致性还是适应文化差异的问题，通过调查不同国家和领域的专业人士，揭示了当前缺乏实证研究支持哪种方法更优。

Abstract

Should multilingual LLMs answer medical questions consistently across input languages, or adapt responses to cultural cues? Existing multilingual medical benchmarks usually assume that medically correct answers should remain consistent across languages and treat cross-lingual variation as model error. In contrast, cultural adaptation research argues that appropriate medical answers may legitimately differ across contexts. We review the multilingual medical NLP literature through these two perspectives, we identify three gaps: limited stakeholder perspectives (e.g., of medical professionals), a lack of empirical evidence on which approach better serves users, and no benchmarks capable of distinguishing universally correct from culture-specific cases. To address the first gap, we survey 356 participants across three stakeholder groups (medical, NLP, and anthropology professionals) in three countries (Germany, Spain, and the United States). Anthropologists consistently favor adaptation, while medical and NLP respondents remain divided, with notable divergence between U.S. and European medical professionals. LLMs prompted with profession and country personas fail to reproduce this variation, overestimating cross-lingual consistency preference among NLP and medical personas. We conclude that neither consistency nor adaptation can currently be considered clearly preferable, highlighting the need for empirical evidence on which approach better serves users across cultural contexts.

Problem

• Cross-lingual Consistency
• Cultural Adaptation
• Medical Questions
• Multilingual LLMs

Innovation

• Cross-lingual Consistency
• Cultural Adaptation
• Stakeholder Perspectives