Aligned Query Expansion: Efficient Query Expansion for Information Retrieval through LLM Alignment
Adam Yang, Gustavo Penha, Enrico Palumbo, Hugues Bouchard

TL;DR
This paper introduces Aligned Query Expansion (AQE), a novel method that fine-tunes large language models to generate more effective query expansions for passage retrieval, reducing costs and improving retrieval accuracy.
Contribution
AQE leverages LLM alignment techniques to directly optimize query expansions for retrieval, eliminating the costly generate-then-filter process and enhancing effectiveness.
Findings
AQE outperforms baseline models in retrieval tasks.
AQE improves effectiveness in both in-domain and out-of-domain settings.
AQE reduces computational costs compared to previous methods.
Abstract
With the breakthroughs in large language models (LLMs), query generation techniques that expand documents and queries with related terms are becoming increasingly popular in the information retrieval field. Such techniques have been shown to improve the effectiveness of traditional lexical retrieval methods by dealing with the vocabulary mismatch problem. Recent work has found that generating queries with a greedy decoding strategy can produce sub-optimal queries, including hallucinations, and proposed to filter out queries before expansion. This `generate-then-filter' approach is costly, as it requires generating multiple queries and applying a relevance model to all of them and does not teach the LLM which of the generated queries is more effective for expansion. To overcome such limitations, we propose Aligned Query Expansion (AQE), a novel approach to enhance query expansion for…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Data Quality and Management
