多語言大型語言模型安全研究現狀：從衡量語言差距到緩解差距

摘要

本文對大型語言模型（LLM）安全研究中的語言多樣性進行了全面分析，揭示了該領域以英語為中心的特質。通過系統性地審查2020年至2024年間在*ACL主要自然語言處理會議及研討會上發表的近300篇文獻，我們發現LLM安全研究存在顯著且日益擴大的語言鴻溝，即便是資源豐富的非英語語言也僅獲得極少關注。我們進一步觀察到，非英語語言鮮少作為獨立語言被研究，且英語安全研究在語言文檔實踐方面表現欠佳。為激勵未來多語言安全研究的開展，我們基於此次調查提出了若干建議，並針對安全評估、訓練數據生成及跨語言安全泛化三個具體方向提出了未來研究建議。基於我們的調查與所提方向，該領域有望為全球多元人口發展出更為穩健、包容的人工智慧安全實踐。

English

This paper presents a comprehensive analysis of the linguistic diversity of LLM safety research, highlighting the English-centric nature of the field. Through a systematic review of nearly 300 publications from 2020--2024 across major NLP conferences and workshops at *ACL, we identify a significant and growing language gap in LLM safety research, with even high-resource non-English languages receiving minimal attention. We further observe that non-English languages are rarely studied as a standalone language and that English safety research exhibits poor language documentation practice. To motivate future research into multilingual safety, we make several recommendations based on our survey, and we then pose three concrete future directions on safety evaluation, training data generation, and crosslingual safety generalization. Based on our survey and proposed directions, the field can develop more robust, inclusive AI safety practices for diverse global populations.

多語言大型語言模型安全研究現狀：從衡量語言差距到緩解差距

The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It

摘要

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