Covering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generation
arXiv:2606.29328v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) typically treats context selection as ranking chunks against a single query embedding. This assumption breaks down for complex queries, such as multi-hop or ambiguous questions, where top-k selection tends to over-cover one semantic aspect while ignoring critical sub-questions. We propose GeoRAG, which recasts context selection as Information Demand Coverage Optimization. GeoRAG builds a multi-dimensional demand distribution through diverse sub-query generation and reverse-validation weighting, then selects
The paper addresses a current limitation in Retrieval-Augmented Generation (RAG) performance for complex queries, indicating ongoing efforts to refine AI models for more sophisticated tasks.
Improving RAG's ability to handle complex queries by optimizing information demand coverage will lead to more accurate and comprehensive AI-generated responses, enhancing the utility of AI in knowledge work.
Context selection in RAG systems can move beyond simple rankings to multi-dimensional demand distributions, making AI applications more robust for nuanced information retrieval.
- · AI developers
- · Enterprise AI users
- · Generative AI platforms
- · AI models lacking advanced context handling
- · Legacy information retrieval systems
AI search and question-answering systems will become significantly more effective at disambiguating complex user requests.
This improved accuracy will accelerate the adoption of RAG-based AI tools across various industries requiring deep knowledge synthesis.
Enhanced RAG capabilities could reduce the need for human experts in certain information-intensive roles, thereby increasing automation in white-collar sectors.
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Read at arXiv cs.AI