OBLIQ-Bench Reveals Retrieval Failures on Latent and Implicit Queries
A significant oversight has been uncovered by researchers in contemporary information retrieval systems: oblique queries that aim to find documents reflecting hidden patterns, like tweets that convey implicit opinions or chat logs that reveal failure modes. The newly established benchmark, OBLIQ-Bench, highlights an imbalance where reasoning LLMs can consistently identify relevance after documents are retrieved, yet retrieval systems often overlook the most pertinent documents. This research outlines three ways obliqueness manifests and presents five oblique search challenges using actual long-tail datasets. The goal of this work is to promote advancements in retrieval frameworks that effectively detect latent patterns and implicit signals.
Key facts
- Oblique queries seek documents instantiating latent patterns.
- Examples include tweets with implicit stances or chat logs with failure modes.
- OBLIQ-Bench is a suite of five oblique search problems.
- It uses real long-tail corpora.
- Reasoning LLMs recognize latent relevance when documents are surfaced.
- Retrieval pipelines fail to surface most relevant documents.
- Three mechanisms of obliqueness are identified.
- The benchmark aims to drive research into new retrieval architectures.
Entities
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