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, amplify misleading vocabulary, or miss terms that distinguish relevant from non-relevant documents. We argue that effective expansion requires retrieval-grounded feedback, not just single-pass generation or unverified iteration. We introduce ADORE (ADapt, Observe, Relevance Evaluate), an iterative framework that turns retrieval outcomes into feedback for the next expansion. At each round, an LLM generates pseudo-...
| Authors | Amin Bigdeli, Negar Arabzadeh, Radin Hamidi Rad et al. |
|---|---|
| Primary category | cs.IR |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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