ReBOL: Retrieval via Bayesian Optimization with Batched LLM Relevance Observations and Query Reformulation
LLM-reranking is limited by the top-k documents retrieved by vector similarity, which neither enables contextual query-document token interactions nor captures multimodal relevance distributions. While LLM query reformulation attempts to improve recall by generating improved or additional queries, it is still followed by vector similarity retrieval. We thus propose to address these top-k retrieval stage failures by introducing ReBOL, which 1) uses LLM query reformulations to initialize a multimodal Bayesian Optimization (BO) posterior over document relevance, and 2) iteratively acquires document batches for LLM query-document relevance scoring followed by posterior updates to optimize rel...
| Authors | Anton Korikov, Scott Sanner |
|---|---|
| Primary category | cs.IR |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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