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New benchmark and adaptation strategies for enterprise search

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DevRev-Search, a new benchmark for passage retrieval in technical customer support, built using an automated pipeline. This benchmark addresses the challenge of adapting retrieval systems in multi-tenant environments where curated relevance labels are scarce and full model re-indexing is impractical. The study also evaluates index-preserving query-only adaptation strategies, demonstrating that parameter-efficient fine-tuning of query encoders offers a strong quality-efficiency trade-off for scalable enterprise search. AI

RANK_REASON The cluster contains a research paper detailing a new benchmark and adaptation strategies for enterprise retrieval systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New benchmark and adaptation strategies for enterprise search

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The cluster contains a research paper detailing a new benchmark and adaptation strategies for enterprise retrieval systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Prateek Jain, Shabari S Nair, Ritesh Goru, Prakhar Agarwal, Ajay Yadav, Yoga Sri Varshan Varadharajan, Constantine Caramanis ·

    Succeeding at Scale: Enterprise Retrieval Benchmark Construction and Index-Preserving Query Adaptation for Multi-Tenant Search

    arXiv:2601.04646v4 Announce Type: replace-cross Abstract: Large-scale multi-tenant retrieval systems generate extensive query logs but lack curated relevance labels for effective domain adaptation, resulting in substantial underutilized "dark data." This challenge is compounded b…

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