arXiv:2606.04646v1 Announce Type: cross Abstract: Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in text. Existing retrieval-augmented generation (RAG) systems are optimized primarily for semantic relevance, but retrieving plausible passages does not guarantee correct query execution. We introduce QO-Bench, a diagnostic benchmark for query-operator question answering over typed event tuples. The benchmark covers 22,984 news articles and 614 corporate events across 18 query templates, evaluated o
The proliferation of RAG systems highlights the limitations of current retrieval methods for complex, structured queries, necessitating specialized benchmarks to diagnose and improve performance.
Improving RAG systems to handle database-style queries over textual data will enable more accurate and reliable extraction of structured information from vast unstructured corpora, critical for various analytical tasks.
The introduction of QO-Bench provides a standardized diagnostic tool to evaluate and enhance the ability of AI systems to perform query-operator-preserving retrieval.
- · AI developers
- · Data analytics companies
- · Enterprise search solutions
- · Legal tech firms
- · Businesses relying solely on semantic relevance for complex queries
- · Current RAG systems without query-operator capabilities
RAG systems will evolve to more accurately answer complex, structured questions from text.
New applications will emerge that leverage the precise extraction of structured event data from unstructured sources, improving decision-making in diverse sectors.
The enhanced ability to 'query' vast textual data like a database could drastically accelerate knowledge discovery and automation in research and business intelligence.
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Read at arXiv cs.AI