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New ADORE framework improves LLM query expansion with iterative feedback

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced ADORE, an iterative framework designed to enhance Large Language Model (LLM)-based query expansion for information retrieval. Unlike generation-driven methods that can lead to retrieval drift, ADORE uses retrieval outcomes as feedback for subsequent expansion rounds. This iterative process involves an LLM generating passages, a retriever assessing corpus response, and a relevance evaluator judging retrieved documents against the original query. ADORE has demonstrated significant performance improvements across multiple benchmarks, including TREC Deep Learning, BEIR, and BRIGHT, outperforming traditional methods like BM25 and prior query expansion techniques. AI

IMPACT This iterative approach to query expansion could lead to more accurate and relevant search results in LLM-powered information retrieval systems.

RANK_REASON The cluster contains an academic paper detailing a new framework for LLM-based query expansion.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New ADORE framework improves LLM query expansion with iterative feedback

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The cluster contains an academic paper detailing a new framework for LLM-based query expansion.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Amin Bigdeli, Negar Arabzadeh, Radin Hamidi Rad, Sajad Ebrahimi, Charles L. A. Clarke, Ebrahim Bagheri ·

    ADORE: Iterative Query Expansion with Retrieval-Grounded Relevance Feedback

    arXiv:2606.13905v1 Announce Type: cross Abstract: LLM-based query expansion improves retrieval by enriching the original query with additional context. Yet most methods remain generation-driven, producing plausible pseudo-documents or expansions without checking how the target co…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Ebrahim Bagheri ·

    ADORE: Iterative Query Expansion with Retrieval-Grounded Relevance Feedback

    LLM-based query expansion improves retrieval by enriching the original query with additional context. Yet most methods remain generation-driven, producing plausible pseudo-documents or expansions without checking how the target corpus responds. This can introduce retrieval drift,…

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