当模型用你的语言思考：控制思考语言会以准确性为代价
When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy
AI总结：
研究发现，强制模型用用户语言推理虽提高可读性但降低准确性，通过微调可缓解此矛盾，揭示当前大型推理模型在多语言推理上的局限性。
摘要：
最近的大型推理模型（LRMs）具有思考痕迹，已在英语推理任务上表现出色。然而，它们在其他语言中思考的能力研究较少。这种能力对现实应用同样重要，因为用户只有在推理痕迹用他们自己的语言表达时，才会发现其有用。我们全面评估了两种领先的LRM家族，发现即使最先进的模型也常常回退到英语或在其他语言中产生碎片化的推理，揭示了多语言推理的显著差距。基于提示的干预措施迫使模型用用户的语言推理，可以提高可读性和监督能力，但会降低答案准确性，暴露了重要的权衡。我们进一步表明，针对训练仅100个示例可以缓解这种不匹配，尽管仍有一些准确性损失。我们的结果突显了当前LRMs在多语言推理方面的有限能力，并指明了未来工作的方向。代码和数据可在https://github.com/Betswish/mCoT-XReasoning上获得。
英文摘要：
Recent Large Reasoning Models (LRMs) with thinking traces have shown strong performance on English reasoning tasks. However, their ability to think in other languages is less studied. This capability is as important as answer accuracy for real world applications because users may find the reasoning trace useful for oversight only when it is expressed in their own language. We comprehensively evaluate two leading families of LRMs on our XReasoning benchmark and find that even the most advanced models often revert to English or produce fragmented reasoning in other languages, revealing a substantial gap in multilingual reasoning. Prompt based interventions that force models to reason in the users language improve readability and oversight but reduce answer accuracy, exposing an important trade off. We further show that targeted post training on just 100 examples mitigates this mismatch, though some accuracy loss remains. Our results highlight the limited multilingual reasoning capabilities of current LRMs and outline directions for future work. Code and data are available at https://github.com/Betswish/mCoT-XReasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
