提升模糊问题问答的准确与效率，通过三步优化检索结果。
Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering
- 先多样化检索内容，覆盖多种可能解释。
- 验证检索质量并动态调整处理策略。
- 兼顾准确率与效率，适合实际部署场景。
检索增强生成（RAG）框架通过检索涵盖所有可能解释的文本片段，生成全面的回答来解决用户查询中的歧义问题。然而，初步研究发现单次检索常因召回片段无法覆盖全部合理解释而导致效果不佳。尽管迭代式RAG可缓解此问题，但显著降低效率。为此，本文提出分步优化的DIVA框架：首先多样化检索结果以覆盖多样解释；随后验证片段质量，并根据质量自适应选择最优处理方式。该方法在提升模糊问题问答系统准确性与鲁棒性的同时，有效增强了整体效率。
原文摘要 · Abstract (English)
The retrieval augmented generation (RAG) framework addresses an ambiguity in user queries in QA systems by retrieving passages that cover all plausible interpretations and generating comprehensive responses based on the passages. However, our preliminary studies reveal that a single retrieval process often suffers from low quality results, as the retrieved passages frequently fail to capture all plausible interpretations. Although the iterative RAG approach has been proposed to address this problem, it comes at the cost of significantly reduced efficiency. To address these issues, we propose the diversify-verify-adapt (DIVA) framework. DIVA first diversifies the retrieved passages to encompass diverse interpretations. Subsequently, DIVA verifies the quality of the passages and adapts the most suitable approach tailored to their quality. This approach improves the QA systems accuracy and robustness by handling low quality retrieval issue in ambiguous questions, while enhancing efficiency.
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