Faux Polyglot：多语言大型语言模型中信息差异性的研究
Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models
AI总结：
本文研究了跨语言RAG信息搜索中LLM的语言偏好，发现其在检索和生成阶段对与查询同语言的信息存在系统性偏见，加剧了多语言模型中的语言鸿沟与信息差异。
摘要：
尽管LLMs的多语言能力为克服语言障碍提供了新机遇，但在语言鸿沟和多语言源之间的知识冲突是已知现象的现实场景中，这些能力能否转化应用？本文研究了在基于跨语言RAG的信息搜索设置中LLM的语言偏好。我们发现，在文档检索和答案生成中，LLMs对与查询语言相同的信息表现出系统性偏见。此外，在查询语言中没有信息的场景下，LLMs更倾向于高资源语言文档，而非低资源语言文档。这种偏见加剧了多语言LLMs中的语言鸿沟。我们的结果突显了多语言LLMs中信息差异性的问题。
英文摘要：
Although the multilingual capability of LLMs offers new opportunities to overcome the language barrier, do these capabilities translate into real-life scenarios where linguistic divide and knowledge conflicts between multilingual sources are known occurrences? In this paper, we studied LLM's linguistic preference in a cross-language RAG-based information search setting. We found that LLMs displayed systemic bias towards information in the same language as the query language in both document retrieval and answer generation. Furthermore, in scenarios where no information is in the language of the query, LLMs prefer documents in high-resource languages during generation, potentially reinforcing the dominant views. Such bias exists for both factual and opinion-based queries. Our results highlight the linguistic divide within multilingual LLMs in information search systems. The seemingly beneficial multilingual capability of LLMs may backfire on information parity by reinforcing language-specific information cocoons or filter bubbles further marginalizing low-resource views.
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。
