跨语言检索增强生成提升多语种知识任务表现
Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task
- 提出跨语言RAG，先翻译检索文档再生成答案
- 在高/低资源语言上均显著提升知识任务准确率
- 适合需要多语言知识推理的应用场景
检索增强生成（RAG）已成为现代自然语言处理的核心技术，通过上下文检索使大语言模型获得更丰富的事实信息。尽管在单语场景（尤其是英语）中表现良好，其在多语言任务中的应用仍待探索。本文研究了多种多语言开放域问答的RAG策略，包括将问题翻译为英文后检索的tRAG，以及直接跨语言检索的MultiRAG。结果表明，tRAG覆盖有限；MultiRAG虽提升效率，但因跨语言内容差异导致不一致。为此，我们提出CrossRAG：将检索到的文档统一翻译成通用语言（如英语），再生成回答。实验显示，CrossRAG在知识密集型任务中显著提升性能，对高资源与低资源语言均有帮助。
原文摘要 · Abstract (English)
Retrieval-augmented generation (RAG) has become a cornerstone of contemporary NLP, enhancing large language models (LLMs) by allowing them to access richer factual contexts through in-context retrieval. While effective in monolingual settings, especially in English, its use in multilingual tasks remains unexplored. This paper investigates the effectiveness of RAG across multiple languages by proposing novel approaches for multilingual open-domain question-answering. We evaluate the performance of various multilingual RAG strategies, including question-translation (tRAG), which translates questions into English before retrieval, and Multilingual RAG (MultiRAG), where retrieval occurs directly across multiple languages. Our findings reveal that tRAG, while useful, suffers from limited coverage. In contrast, MultiRAG improves efficiency by enabling multilingual retrieval but introduces inconsistencies due to cross-lingual variations in the retrieved content. To address these issues, we propose Crosslingual RAG (CrossRAG), a method that translates retrieved documents into a common language (e.g., English) before generating the response. Our experiments show that CrossRAG significantly enhances performance on knowledge-intensive tasks, benefiting both high-resource and low-resource languages.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。