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New framework audits PRF in information retrieval using LLMs and user feedback

By PulseAugur Editorial · [1 sources] ·

A new research paper proposes a two-stage framework to address the opacity of Pseudo-Relevance Feedback (PRF) in information retrieval. The framework first involves a participatory audit with 108 users, which revealed that PRF only benefits about 20.9% of queries, while negatively impacting 25.6%. The second stage utilizes LLM-based rerankers to automatically predict user-derived labels, allowing for scalable inspection of PRF decisions. This approach aims to transform PRF from an opaque component into an auditable, user-grounded system. AI

IMPACT This research could lead to more transparent and effective information retrieval systems by improving how PRF is applied.

RANK_REASON The cluster contains an academic paper detailing a new framework for information retrieval. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.IR (Information Retrieval) →

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

New framework audits PRF in information retrieval using LLMs and user feedback

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The cluster contains an academic paper detailing a new framework for information retrieval. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Iadh Ounis ·

    Explaining When PRF Fails: Participatory Auditing for Selective Query Expansion

    Pseudo-Relevance Feedback (PRF) improves retrieval effectiveness on average, but harms a substantial fraction of queries through query drift, an asymmetry hidden by aggregate offline metrics. Existing Selective PRF (sPRF) approaches typically rely on Query Performance Prediction …

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