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English(EN) Skill Issue: Are Skills Language-Invariant in LLMs?

大型语言模型在多语言自我对抗中表现出语言依赖的技能差距

作者 PulseAugur 编辑部 · [3 个来源] ·

一篇题为“技能问题：技能是否在大型语言模型中具有语言不变性？”的新研究论文，探讨了大型语言模型（LLMs）在不同语言中的表现差异。研究使用了一个名为TextArena的文本游戏中的多语言自我对抗设置，发现同一个LLM根据其使用的语言界面，会表现出不同的技能水平和策略倾向。在英语交互时性能通常最高，而在希伯来语等语言中性能最低，并注意到在空间推理和决策方面存在特定缺陷。研究表明，语言会显著影响LLM的决策过程，阻碍了真正多语言模型的开发。 AI

影响 识别出开发真正多语言LLM的一个重大障碍，表明语言选择会影响模型性能和策略。

排序理由 该集群包含一篇详细介绍LLM行为新研究发现的学术论文。

在 Hugging Face Daily Papers 阅读 →

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大型语言模型在多语言自我对抗中表现出语言依赖的技能差距

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报道来源 [3]

  1. arXiv cs.CL TIER_1 English(EN) · Bobby Cheng, Adam Gaber, Zhengyuan Liu, Catherine Arnett, Omer Goldman, Cheston Tan, Leshem Choshen ·

    技能问题：技能在大型语言模型中是否与语言无关？

    arXiv:2608.25832v1 Announce Type: new Abstract: Large language models access knowledge inconsistently across languages, but to what extent do they differ in their skill sets when interacting with different languages? This work quantifies cross-lingual skill inconsistency orthogon…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    技能问题：技能在大型语言模型中是否与语言无关？

    Large language models access knowledge inconsistently across languages, but to what extent do they differ in their skill sets when interacting with different languages? This work quantifies cross-lingual skill inconsistency orthogonally from knowledge and general benchmark perfor…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    技能问题：技能在大型语言模型中是否与语言无关？

    Multilingual self-play reveals that large language models exhibit significant cross-lingual skill inconsistencies in reasoning and strategy, partly recoverable by altering intermediate reasoning language.

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