让大模型学会用本地知识推理，提升非英语表现
EMCEE: Improving Multilingual Capability of LLMs via Bridging Knowledge and Reasoning with Extracted Synthetic Multilingual Context
- 从模型内部提取语言特有知识作为上下文
- 在多语言任务上平均提升16.4%，低资源语言达31.7%
- 适合需要跨语言精准理解的场景
大型语言模型在众多任务中取得显著进展，但其对英语训练数据的高度依赖导致非英语语言性能严重下降。现有跨语言提示方法多聚焦于将查询重述为英语或增强推理能力，却常忽略特定语言与文化背景下的语境信息。为此，我们提出EMCEE（提取合成多语言上下文并融合）框架，通过显式从模型自身提取并利用与查询相关的知识，增强大模型的多语言能力。EMCEE首先提取合成上下文以揭示模型内隐的、语言特异的知识，再通过基于判断的选择机制，动态融合这些上下文信息与推理输出。在涵盖多种语言和任务的四个多语言基准上的大量实验表明，EMCEE持续优于以往方法，在整体上实现16.4%的相对提升，低资源语言中提升达31.7%。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have achieved impressive progress across a wide range of tasks, yet their heavy reliance on English-centric training data leads to significant performance degradation in non-English languages. While existing multilingual prompting methods emphasize reformulating queries into English or enhancing reasoning capabilities, they often fail to incorporate the language- and culture-specific grounding that is essential for some queries. To address this limitation, we propose EMCEE (Extracting synthetic Multilingual Context and merging), a simple yet effective framework that enhances the multilingual capabilities of LLMs by explicitly extracting and utilizing query-relevant knowledge from the LLM itself. In particular, EMCEE first extracts synthetic context to uncover latent, language-specific knowledge encoded within the LLM, and then dynamically merges this contextual insight with reasoning-oriented outputs through a judgment-based selection mechanism. Extensive experiments on four multilingual benchmarks covering diverse languages and tasks demonstrate that EMCEE consistently outperforms prior approaches, achieving an average relative improvement of 16.4% overall and 31.7% in low-resource languages.
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