大推理模型在不同脚本间转移参数知识时面临困难
Large Reasoning Models Struggle to Transfer Parametric Knowledge Across Scripts
AI总结：
研究发现，大模型在跨语言知识转移中主要受脚本差异影响，通过分析数据和实验表明，提升模型对转写歧义的推理能力可改善跨脚本知识转移。
摘要：
本文分析了大现代推理LLM在跨语言知识转移中的不足，发现知识转移失败的主要原因是脚本障碍。通过观察分析发现，脚本匹配而非语言或语系是知识转移失败的主要预测因素。进一步实验表明，提供问题关键实体可显著提升跨脚本问题的表现。研究提出LLM在测试时可能具备更好的推理能力，并通过合成生成管道设计SFT样本，鼓励模型在推理时更好地处理转写歧义。结果表明，训练模型提升推理能力可缩小跨脚本转移差距，从而有潜力改进训练后的跨语言参数知识转移。
英文摘要：
In this work, we analyze shortcomings in cross-lingual knowledge transfer in large, modern reasoning LLMs. We demonstrate that the perceived gap in knowledge transfer is primarily a script barrier. First, we conduct an observational data analysis on the performance of thinking models on two datasets with local knowledge from around the world, ECLeKTic and MultiLoKo. Our regression analysis shows that script match - not language or family - is the primary predictor of knowledge transfer failure once model capability and question difficulty are accounted for. We further this finding by providing the LLMs with the key entities of the questions in their source language and find that this disproportionately improves cross-script questions. We then posit that these LLMs could be reasoning better at test-time. To evaluate this, we develop a synthetic generation pipeline to design SFT samples to encourage the model to better reason about transliteration ambiguities when trying to fetch parametric knowledge at inference-time. We show that teaching two models to reason better reduces the cross-script transfer gap. As a result, we conclude that there is potential to improve cross-lingual parametric knowledge transfer during post-training.
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