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在LLM中,并非所有语言平等:通过跨语言思维提示来提高多语能力

Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting

May 11, 2023
作者: Haoyang Huang, Tianyi Tang, Dongdong Zhang, Wayne Xin Zhao, Ting Song, Yan Xia, Furu Wei
cs.AI

摘要

大型语言模型(LLMs)展示了令人印象深刻的多语言能力,但它们在不同语言之间的性能差异很大。在这项工作中,我们引入了一种简单而有效的方法,称为跨语言思维提示(XLT),系统地提高LLMs的多语言能力。具体而言,XLT是一个通用的模板提示,可以激发跨语言和逻辑推理能力,以增强不同语言任务的性能。我们在涵盖推理、理解和生成任务的7个典型基准上进行了全面评估,涵盖了高资源和低资源语言。实验结果表明,XLT不仅显著提高了各种多语言任务的性能,还显著缩小了不同语言中每个任务的平均性能和最佳性能之间的差距。值得注意的是,XLT在算术推理和开放领域问答任务中带来了超过10个平均改进点。
English
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.
PDF10December 15, 2024