A comprehensive survey that organizes query expansion methods in the PLM/LLM era along four design dimensions, synthesizes application patterns, and outlines future directions.
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LTRR learns to rank a pool of retrievers by their expected contribution to RAG answer correctness and shows that query-dependent selection beats the best single retriever on QA benchmarks.
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Query Expansion in the Age of Pre-trained and Large Language Models: A Comprehensive Survey
A comprehensive survey that organizes query expansion methods in the PLM/LLM era along four design dimensions, synthesizes application patterns, and outlines future directions.
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LTRR: Learning To Rank Retrievers for LLMs
LTRR learns to rank a pool of retrievers by their expected contribution to RAG answer correctness and shows that query-dependent selection beats the best single retriever on QA benchmarks.