在LLMs中并非所有语言生而平等：通过跨语言思考提示提升多语言能力
Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting
AI总结：
针对LLMs多语言能力不均的问题，提出跨语言思考提示（XLT）方法，通过通用模板激发跨语言与逻辑推理，在7个基准上显著提升多语言任务表现并缩小语言间性能差距。
摘要：
大型语言模型（LLMs）展现出令人印象深刻的多语言能力，但其在不同语言上的表现差异显著。在本研究中，我们提出了一种简单而有效的方法，称为跨语言思考提示（XLT），以系统性地提升LLMs的多语言能力。具体而言，XLT是一种通用模板提示，它激发跨语言与逻辑推理技能，从而增强跨语言的任务表现。我们在7个涉及推理、理解与生成任务的典型基准上进行了全面评估，涵盖高资源与低资源语言。实验结果表明，XLT不仅显著提升了多种多语言任务的性能，还大幅缩小了不同语言下各任务平均表现与最佳表现之间的差距。值得注意的是，XLT在算术推理与开放域问答任务中带来了超过10分的平均提升。
英文摘要：
Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. In this work, we introduce a simple yet effective method, called cross-lingual-thought prompting (XLT), to systematically improve the multilingual capability of LLMs. Specifically, XLT is a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages. We conduct comprehensive evaluations on 7 typical benchmarks related to reasoning, understanding, and generation tasks, covering both high-resource and low-resource languages. Experimental results show that XLT not only remarkably enhances the performance of various multilingual tasks but also significantly reduces the gap between the average performance and the best performance of each task in different languages. Notably, XLT brings over 10 points of average improvement in arithmetic reasoning and open-domain question-answering tasks.
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。
