DIVERGE: 面向开放式信息检索的多样性增强RAG
DIVERGE: Diversity-Enhanced RAG for Open-Ended Information Seeking
AI总结：
针对现有RAG系统忽略开放式信息检索中多样性需求的问题，提出Diverge框架，通过迭代反思引导的多样化视角探索和多样性感知检索支持，在保持质量的同时将多样性提升约2倍。
摘要：
现有的检索增强生成（RAG）系统通常假设每个查询只有一个正确答案。这种假设忽略了开放式信息检索场景，其中多个合理的答案是有价值的，并且多样性对于创造力、公平性和信息的包容性访问至关重要。我们表明，标准RAG系统未能充分利用多样化的检索上下文：简单地增加检索多样性并不一定会导致多样化的生成。为了解决这一局限性，我们提出了Diverge，一个即插即用的智能体RAG框架，通过迭代、反思引导的多样化视角探索和多样性感知检索支持来改善多样性与质量的权衡。我们进一步引入了用于表征开放式问答中多样性与质量权衡的评估指标。在多个真实世界数据集和骨干LLM上的实验表明，Diverge在竞争基线中实现了最佳的权衡，将多样性提高了约2倍，且没有明显的质量下降。这些结果揭示了当前RAG系统的系统性局限，并展示了显式多样性建模的价值。
英文摘要：
Existing retrieval-augmented generation (RAG) systems often assume that each query has a single correct answer. This assumption overlooks open-ended information-seeking scenarios where multiple plausible answers are valuable, and where diversity is important for creativity, fairness, and inclusive access to information. We show that standard RAG systems fail to fully use diverse retrieved contexts: simply increasing retrieval diversity does not necessarily lead to diverse generations. To address this limitation, we propose Diverge, a plug-and-play agentic RAG framework that improves the diversity--quality trade-off through iterative, reflection-guided exploration of diverse viewpoints and diversity-aware retrieval support. We further introduce evaluation metrics for characterizing the diversity-quality trade-off in open-ended question answering. Experiments across multiple real-world datasets and backbone LLMs show that Diverge achieves the best trade-off among competitive baselines, increasing diversity by $\sim2\times$ without noticeable quality degradation. These results reveal a systematic limitation of current RAGs and show the value of explicit diversity modeling.
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。
