arXiv:2602.00238v2 Announce Type: replace-cross Abstract: 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 pl
The rapid advancement and widespread adoption of RAG systems highlight existing limitations, driving research into more nuanced information-seeking approaches as AI systems grow more complex.
Improving RAG systems for open-ended queries enhances the utility, fairness, and creativity of AI applications, moving beyond single-answer constraints to support more complex human information needs.
Standard RAG systems are shown to inadequately leverage diverse retrieved contexts; new methodologies like DIVERGE aim to overcome this by actively promoting diversity in generative outputs.
- · AI researchers and developers
- · Creative industries
- · Information retrieval systems
- · Users seeking comprehensive answers
- · Legacy RAG systems
- · Applications requiring only single-point answers
AI models will provide more varied and contextually rich responses to complex queries.
This could lead to more inclusive and less biased AI-generated content across various domains.
The enhanced diversity in AI outputs might foster greater human creativity and critical thinking by offering multiple plausible perspectives.
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Read at arXiv cs.LG