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New EXCISE method enhances AI retrieval systems for exclusion queries

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel query-side exclusion method called EXCISE to improve the performance of late-interaction retrieval systems, which traditionally struggle with exclusion queries. EXCISE operates by identifying and re-embedding excluded topics at query time, without altering the index. This approach significantly boosts exclusion success rates and Boolean NOT accuracy across various collections and backbones, outperforming existing fine-tuned cross-encoders. The team also released X-BENCH, a new benchmark designed to evaluate explicit, implicit, and compound exclusion queries. AI

IMPACT Improves the accuracy and utility of AI-powered search and information retrieval systems, particularly for complex exclusion queries.

RANK_REASON Academic paper detailing a new method for information retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New EXCISE method enhances AI retrieval systems for exclusion queries

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Academic paper detailing a new method for information retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Adam Jatowt ·

    EXCISE: Query-Side Exclusion for Late-Interaction Retrieval

    Late-interaction retrievers handle exclusion queries poorly. When a user asks for X but not Z, the additive MaxSim score promotes documents covering Z, a problem we call exclusion inversion. We show that no readout of the frozen vectors recovers the constraint, because the diffic…

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