多语言医疗问答中跨语言证据的影响
Effects of Cross-lingual Evidence in Multilingual Medical Question Answering
AI总结：
本文研究了多语言医疗问答中跨语言证据的作用，探讨了不同规模模型在三种外部证据源下的表现，发现大模型在英语中表现更优，而低资源语言需结合英式和目标语言检索以获得相近准确率。
摘要：
本文研究了在高资源语言（英语、西班牙语、法语、意大利语）和低资源语言（巴斯克语、哈萨克语）之间进行多语言医疗问答时，跨语言证据的影响。我们评估了三种外部证据源在不同规模模型中的表现：专门的医学知识库、网络检索内容以及大语言模型的参数知识解释。此外，我们进行了多语言、单语言和跨语言检索实验。结果表明，大模型在英语基准评估中表现更优。当引入外部知识时，英语网络检索内容对高资源语言最有效。相反，对于低资源语言，结合英语和目标语言的检索策略最有效，其准确率可与高资源语言结果相媲美。这些发现挑战了外部知识系统性提升性能的假设，揭示了有效策略依赖于语言资源来源和模型规模。此外，专门的医学知识源如PubMed存在局限：虽然它们提供权威专家知识，但缺乏足够的多语言覆盖。
英文摘要：
This paper investigates Multilingual Medical Question Answering across high-resource (English, Spanish, French, Italian) and low-resource (Basque, Kazakh) languages. We evaluate three types of external evidence sources across models of varying size: curated repositories of specialized medical knowledge, web-retrieved content, and explanations from LLM's parametric knowledge. Moreover, we conduct experiments with multilingual, monolingual and cross-lingual retrieval. Our results demonstrate that larger models consistently achieve superior performance in English across baseline evaluations. When incorporating external knowledge, web-retrieved data in English proves most beneficial for high-resource languages. Conversely, for low-resource languages, the most effective strategy combines retrieval in both English and the target language, achieving comparable accuracy to high-resource language results. These findings challenge the assumption that external knowledge systematically improves performance and reveal that effective strategies depend on both the source of language resources and on model scale. Furthermore, specialized medical knowledge sources such as PubMed are limited: while they provide authoritative expert knowledge, they lack adequate multilingual coverage
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。
