CORAL：面向文化对齐的多语言RAG自适应检索循环
CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG
AI总结：
CORAL通过迭代优化检索空间和查询，提升多语言检索生成在文化相关性上的表现，实验显示在低资源语言上准确率提升3.58%。
摘要：
多语言检索增强生成（mRAG）通常在固定检索空间中实现，通常通过查询或文档翻译或多语言嵌入向量表示。然而，这种做法可能无法应对文化相关的查询，其中检索条件不匹配可能发生。即使强大的检索器和生成器，当从不恰当的语言或地域上下文中获取证据时，也可能难以生成文化相关的答案。为此，我们引入CORAL（COntext-aware Retrieval with Agentic Loop，一种针对mRAG的自适应检索方法，能够根据证据质量迭代优化检索空间（语料库）和检索探针（查询）。整个过程包括：（1）选择语料库，（2）检索文档，（3）评估证据的相关性和文化对齐性，（4）检查充分性。如果检索到的文档不足以正确回答查询，系统（5）重新选择语料库并重写查询。在两个文化问答基准测试中，CORAL在低资源语言上相对于最强基线实现了高达3.58%的准确率提升。
英文摘要：
Multilingual retrieval-augmented generation (mRAG) is often implemented within a fixed retrieval space, typically via query or document translation or multilingual embedding vector representations. However, this approach may be inadequate for culturally grounded queries, in which retrieval-condition misalignment may occur. Even strong retrievers and generators may struggle to produce culturally relevant answers when sourcing evidence from inappropriate linguistic or regional contexts. To this end, we introduce CORAL (COntext-aware Retrieval with Agentic Loop, an adaptive retrieval methodology for mRAG that enables iterative refinement of both the retrieval space (corpora) and the retrieval probe (query) based on the quality of the evidence. The overall process includes: (1) selecting corpora, (2) retrieving documents, (3) critiquing evidence for relevance and cultural alignment, and (4) checking sufficiency. If the retrieved documents are insufficient to answer the query correctly, the system (5) reselects corpora and rewrites the query. Across two cultural QA benchmarks, CORAL achieves up to a 3.58%p accuracy improvement on low-resource languages relative to the strongest baselines.
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。
