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New SAFE-QE method tackles LLM hallucinated terms in query expansion

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method called SAFE-QE to address the issue of hallucinated terms generated by large language models (LLMs) during query expansion. This training-free, retriever-agnostic approach identifies and down-weights terms that appear due to dominant hallucinatory associations rather than actual corpus support. By balancing consistency and breadth, SAFE-QE aims to improve information retrieval by reducing noise without sacrificing ranking quality, as demonstrated on TREC and BEIR benchmark collections. AI

IMPACT This research offers a method to improve the reliability of LLM-generated query expansions, potentially leading to more accurate information retrieval systems.

RANK_REASON The cluster describes a new method proposed in an academic paper for improving LLM query expansion.

Read on arXiv cs.IR (Information Retrieval) →

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

New SAFE-QE method tackles LLM hallucinated terms in query expansion

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The cluster describes a new method proposed in an academic paper for improving LLM query expansion.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Amit Kumar Jaiswal, Sagar Uprety, Prayag Tiwari ·

    Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion

    arXiv:2610.13810v1 Announce Type: cross Abstract: Large language models (LLMs) are now a standard source of query expansion terms but they hallucinate. They inject non existent entities and off-topic terms that add ranking noise and can hurt strong retrievers. Existing remedies s…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Prayag Tiwari ·

    Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion

    Large language models (LLMs) are now a standard source of query expansion terms but they hallucinate. They inject non existent entities and off-topic terms that add ranking noise and can hurt strong retrievers. Existing remedies such as corpus grounding, mutual verification and p…

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