Papers with expansions
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets (2024.findings-eacl)
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| Challenge: | Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. |
| Approach: | They conduct the first comprehensive analysis of large language models (LMs) for query or document expansion. |
| Outcome: | The proposed expansions improve retrieval performance for weaker models but harm stronger models. |
Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval (2025.naacl-long)
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| Challenge: | Existing methods to generate query expansions focus on enhancing textual similarities between search queries and document corpus, overlooking document relations. |
| Approach: | They propose a knowledge-aware query expansion framework augmenting LLMs with structured document relations from knowledge graph (KG) they leverage document texts as rich KG node representations and use document-based relation filtering for their method. |
| Outcome: | The proposed framework augments LLMs with structured document relations from knowledge graph (KG) Extensive experiments on three datasets of diverse domains show the advantages compared against state-of-the-art methods on textual and relational semi-structured retrieval. |