DIVERGE: Diversity-Enhanced Retrieval-Augmented Generation for Open-Ended Information Seeking

Tianyi Hu, Niket Tandon, Akhil Arora

Track: Main Papers Track (6 to 9 pages)

Keywords: Large Language Models, Retrieval-Augmented Generation, Fairness, Diversity, Diversity-quality trade-off, Open-ended Information Seeking, Agentic RAG

TL;DR: Diversity-Enhanced Retrieval-Augmented Generation for Open-Ended Information Seeking

Abstract: Existing retrieval-augmented generation (RAG) systems are primarily designed under the assumption that each query has a single correct answer. This overlooks common information-seeking scenarios with multiple plausible answers, where diversity is essential to avoid collapsing to a single dominant response, thereby constraining creativity and compromising fair and inclusive information access. Our analysis reveals a commonly overlooked limitation of standard RAG systems: they underutilize retrieved context diversity, such that increasing retrieval diversity alone does not yield diverse generations. To address this limitation, we propose DIVERGE, a plug-and-play agentic RAG framework with novel reflection-guided generation and memory-augmented iterative refinement, which promotes diverse viewpoints while preserving answer quality. We introduce novel metrics tailored to evaluating the diversity–quality trade-off in open-ended questions, and show that they correlate well with human judgments. We demonstrate that DIVERGE achieves the best diversity–quality trade-off compared to competitive baselines and previous SOTA methods on the real-world Infinity-Chat dataset, substantially improving diversity while maintaining quality. More broadly, our results reveal a systematic limitation of current LLM-based systems for open-ended information-seeking and show that explicitly modeling diversity can mitigate it. Our code is available at https://anonymous.4open.science/r/diverge-277B/README.

Submission Number: 19