TCDE: Topic-Centric Dual Expansion of Queries and Documents with Large Language Models for Information Retrieval

• URL: http://arxiv.org/abs/2512.17164v1
• Date: Fri, 19 Dec 2025 01:57:17 GMT
• Title: TCDE: Topic-Centric Dual Expansion of Queries and Documents with Large Language Models for Information Retrieval
• Authors: Yu Yang, Feng Tian, Ping Chen,
• Abstract summary: We propose TCDE, a dual expansion strategy that leverages large language models for topic-centric enrichment on both queries and documents.<n>In TCDE, we design two distinct prompt templates for processing each query and document. On the query side, an LLM is guided to identify distinct sub-topics within each query and generate a focused pseudo-document for each sub-topic.<n> Experiments on two challenging benchmarks, TREC Deep Learning and BEIR, demonstrate that TCDE achieves substantial improvements over strong state-of-the-art expansion baselines.
• Score: 9.300741539959278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Query Expansion (QE) enriches queries and Document Expansion (DE) enriches documents, and these two techniques are often applied separately. However, such separate application may lead to semantic misalignment between the expanded queries (or documents) and their relevant documents (or queries). To address this serious issue, we propose TCDE, a dual expansion strategy that leverages large language models (LLMs) for topic-centric enrichment on both queries and documents. In TCDE, we design two distinct prompt templates for processing each query and document. On the query side, an LLM is guided to identify distinct sub-topics within each query and generate a focused pseudo-document for each sub-topic. On the document side, an LLM is guided to distill each document into a set of core topic sentences. The resulting outputs are used to expand the original query and document. This topic-centric dual expansion process establishes semantic bridges between queries and their relevant documents, enabling better alignment for downstream retrieval models. Experiments on two challenging benchmarks, TREC Deep Learning and BEIR, demonstrate that TCDE achieves substantial improvements over strong state-of-the-art expansion baselines. In particular, on dense retrieval tasks, it outperforms several state-of-the-art methods, with a relative improvement of 2.8\% in NDCG@10 on the SciFact dataset. Experimental results validate the effectiveness of our topic-centric and dual expansion strategy.

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