PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Document Retrieval

• URL: http://arxiv.org/abs/2510.09897v1
• Date: Fri, 10 Oct 2025 22:21:49 GMT
• Title: PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Document Retrieval
• Authors: Wonbin Kweon, Runchu Tian, SeongKu Kang, Pengcheng Jiang, Zhiyong Lu, Jiawei Han, Hwanjo Yu,
• Abstract summary: Pairwise Semantic Matching (PairSem) is a framework that represents relevant semantics as entity-aspect pairs.<n>Experiments on multiple datasets and retrievers demonstrate that PairSem significantly improves retrieval performance.
• Score: 41.064644438540135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scientific document retrieval is a critical task for enabling knowledge discovery and supporting research across diverse domains. However, existing dense retrieval methods often struggle to capture fine-grained scientific concepts in texts due to their reliance on holistic embeddings and limited domain understanding. Recent approaches leverage large language models (LLMs) to extract fine-grained semantic entities and enhance semantic matching, but they typically treat entities as independent fragments, overlooking the multi-faceted nature of scientific concepts. To address this limitation, we propose Pairwise Semantic Matching (PairSem), a framework that represents relevant semantics as entity-aspect pairs, capturing complex, multi-faceted scientific concepts. PairSem is unsupervised, base retriever-agnostic, and plug-and-play, enabling precise and context-aware matching without requiring query-document labels or entity annotations. Extensive experiments on multiple datasets and retrievers demonstrate that PairSem significantly improves retrieval performance, highlighting the importance of modeling multi-aspect semantics in scientific information retrieval.

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