GOLFer: Smaller LMs-Generated Documents Hallucination Filter & Combiner for Query Expansion in Information Retrieval
Year: 2025
Type: conference-paper
Abstract: Large language models (LLMs)-based query expansion for information retrieval augments queries with generated hypothetical documents with LLMs.However, its performance relies heavily on the scale of the language models (LMs), necessitating larger, more advanced LLMs.This approach is costly, computationally intensive, and often has limited accessibility.To address these limitations, we introduce GOLFer -Smaller LMs-Generated Documents Hallucination Filter & Combiner -a novel method leveraging smaller opensource LMs for query expansion.GOLFer comprises two modules: a hallucination filter and a documents combiner.The former detects and removes non-factual and inconsistent sentences in generated documents, a common issue with smaller LMs, while the latter combines the filtered content with the query using a weight vector to balance their influence.We evaluate GOLFer alongside dominant LLMbased query expansion methods on three web search and ten low-resource datasets.Experimental results demonstrate that GOLFer consistently outperforms other methods using smaller LMs, and maintains competitive performance against methods using large-size LLMs, demonstrating its effectiveness.The code for our method is publicly available at https://github.com/liuliuyuan6/GOLFer.
Authors: Lingyuan Liu, Mengxiang Zhang
Institutions: City University of Hong Kong, University of Hong Kong
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412888918