推理通用语：多语言AI的双刃剑
The Reasoning Lingua Franca: A Double-Edged Sword for Multilingual AI
AI总结：
本研究探讨了大型推理模型在多语言环境下的推理能力，发现英语推理在准确性上表现更优，但可能因翻译错误导致问题，揭示了多语言AI的双刃剑特性。
摘要：
大型推理模型（LRMs）在数学、科学及其他问答任务上表现出色，但其多语言推理能力仍待深入探索。当面对非英语问题时，LRMs往往默认用英语进行推理，引发了关于可解释性和语言文化细微差别处理的担忧。我们系统地比较了LRM在英语与问题语言中的推理表现。评估涵盖两项任务：MGSM和GPQA Diamond。除了测量答案准确性外，我们还分析了推理轨迹中的认知属性。我们发现，英语推理轨迹中这些认知行为的出现显著更高，且用英语推理通常能获得更高的最终答案准确性，随着任务复杂性增加，性能差距也扩大。然而，这种以英语为中心的策略容易出现关键失败模式——
英文摘要：
Large Reasoning Models (LRMs) achieve strong performance on mathematical, scientific, and other question-answering tasks, but their multilingual reasoning abilities remain underexplored. When presented with non-English questions, LRMs often default to reasoning in English, raising concerns about interpretability and the handling of linguistic and cultural nuances. We systematically compare an LRM's reasoning in English versus the language of the question. Our evaluation spans two tasks: MGSM and GPQA Diamond. Beyond measuring answer accuracy, we also analyze cognitive attributes in the reasoning traces. We find that English reasoning traces exhibit a substantially higher presence of these cognitive behaviors, and that reasoning in English generally yields higher final-answer accuracy, with the performance gap increasing as tasks become more complex. However, this English-centric strategy is susceptible to a key failure mode - getting "Lost in Translation," where translation steps lead to errors that would have been avoided by reasoning in the language of the question.
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。
