CORAL通过动态调整检索范围和查询，提升多语言问答的文化相关性。
CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG
- 基于证据质量迭代优化检索语料和查询
- 在低资源语言上最高提升3.58个百分点准确率
- 适合需要文化敏感性的跨语言问答场景
多语言检索增强生成（mRAG）通常在固定检索空间中实现，依赖查询或文档翻译，或使用多语言嵌入向量。然而，这种方法在文化语境驱动的查询中可能失效，导致检索与上下文不匹配。即使强检索器和生成器，也可能因来自不当语言或地区背景的证据而无法生成文化相关的回答。为此，我们提出CORAL（COntext-aware Retrieval with Agentic Loop），一种自适应的mRAG检索方法，可基于证据质量迭代地优化检索语料库和检索查询。整个流程包括：(1) 选择语料库，(2) 检索文档，(3) 评估证据的相关性与文化一致性，(4) 检查充分性。若文档不足以正确回答问题，则系统(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相较最强基线，在低资源语言上最高提升3.58个百分点准确率。
原文摘要 · Abstract (English)
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.
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