OBLIQ-Bench: Exposing Overlooked Bottlenecks in Modern Retrievers with Latent and Implicit Queries
arXiv:2605.06235v2 Announce Type: replace-cross Abstract: Retrieval benchmarks are increasingly saturating, but we argue that efficient search is far from a solved problem. We identify a class of queries we call oblique, which seek documents that instantiate a latent pattern, like finding all tweets that express an implicit stance, chat logs that demonstrate a particular failure mode, or transcripts that match an abstract scenario. We study three mechanisms through which obliqueness may arise and introduce OBLIQ-Bench, a suite of five oblique search problems over real long-tail corpora. OBLIQ-
The proliferation of complex, unstructured data and the limitations of current retrieval benchmarks necessitate new approaches to information access, particularly for nuanced patterns.
This work highlights critical shortcomings in modern information retrieval, suggesting that current systems are not sufficient for understanding implicit user intent and complex data relationships.
The introduction of OBLIQ-Bench shifts the focus of retrieval evaluation to 'oblique' queries, pushing for more sophisticated latent pattern recognition rather than simple keyword matches.
- · AI research labs focused on advanced retrieval
- · Generative AI platforms
- · Companies with complex internal knowledge bases
- · NLP researchers
- · Search engines reliant solely on explicit query matching
- · Simplified retrieval-augmented generation (RAG) systems
- · Organizations with siloed, untaggable data
- · Legacy information management systems
The new OBLIQ-Bench will drive innovation in retrieval-augmented generation and contextual understanding for AI models.
Improved latent pattern recognition will enhance the capabilities of AI agents in complex environments, making them more effective at discerning user intent.
This could lead to a re-evaluation of data structuring and annotation practices, as systems become capable of extracting meaning from less explicitly organized information.
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Read at arXiv cs.AI