YpathRAG:A Retrieval-Augmented Generation Framework and Benchmark for Pathology

• URL: http://arxiv.org/abs/2510.08603v1
• Date: Tue, 07 Oct 2025 08:47:59 GMT
• Title: YpathRAG:A Retrieval-Augmented Generation Framework and Benchmark for Pathology
• Authors: Deshui Yu, Yizhi Wang, Saihui Jin, Taojie Zhu, Fanyi Zeng, Wen Qian, Zirui Huang, Jingli Ouyang, Jiameng Li, Zhen Song, Tian Guan, Yonghong He,
• Abstract summary: We build a pathology vector database covering 28 subfields and 1.53 million paragraphs.<n>We present YpathRAG, a pathology-oriented RAG framework with dual-channel hybrid retrieval.<n>We also release two evaluation benchmarks, YpathR and YpathQA-M.
• Score: 16.03995342015096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) excel on general tasks yet still hallucinate in high-barrier domains such as pathology. Prior work often relies on domain fine-tuning, which neither expands the knowledge boundary nor enforces evidence-grounded constraints. We therefore build a pathology vector database covering 28 subfields and 1.53 million paragraphs, and present YpathRAG, a pathology-oriented RAG framework with dual-channel hybrid retrieval (BGE-M3 dense retrieval coupled with vocabulary-guided sparse retrieval) and an LLM-based supportive-evidence judgment module that closes the retrieval-judgment-generation loop. We also release two evaluation benchmarks, YpathR and YpathQA-M. On YpathR, YpathRAG attains Recall@5 of 98.64%, a gain of 23 percentage points over the baseline; on YpathQA-M, a set of the 300 most challenging questions, it increases the accuracies of both general and medical LLMs by 9.0% on average and up to 15.6%. These results demonstrate improved retrieval quality and factual reliability, providing a scalable construction paradigm and interpretable evaluation for pathology-oriented RAG.

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