arXiv:2606.10621v1 Announce Type: cross Abstract: Modern retrieval increasingly relies on dense and learned-sparse neural models that are effective but require encoding the entire corpus into a specialized index, rebuilt whenever the model changes. Lexical retrievers like BM25 stay efficient and transparent on a standard inverted index that need not change as models evolve, but suffer from vocabulary mismatch. LLM query rewriting can help, yet prompted rewriters emit well-formed but retrieval-ineffective or harmful-terms, and training against a retrieval reward gives only delayed, sequence-lev
The increasing reliance on dense neural models for retrieval, coupled with their inherent limitations in efficiency and adaptability, necessitates advancements in query optimization techniques for information retrieval.
Improving the efficacy of lexical retrievers through AI-driven query optimization can significantly enhance information access across various applications without requiring continuous re-indexing of large corpora.
Retrieval systems can become more efficient and adaptable, leveraging AI to bridge the 'vocabulary mismatch' gap in traditional lexical models without the computational overhead of constant re-encoding.
- · Information retrieval platforms
- · Search engine providers
- · Knowledge management systems
- · AI model developers
- · Systems heavily reliant on frequent, full corpus re-encoding
- · Inefficient prompt engineers for retrieval systems
More accurate and faster search results for users.
Reduced operational costs for maintaining large-scale retrieval systems.
Potential for new AI-powered information services that are currently too compute-intensive or slow.
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Read at arXiv cs.AI