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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.08603 |
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| _version_ | 1866914085376884736 |
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| author | Yu, Deshui Wang, Yizhi Jin, Saihui Zhu, Taojie Zeng, Fanyi Qian, Wen Huang, Zirui Ouyang, Jingli Li, Jiameng Song, Zhen Guan, Tian He, Yonghong |
| author_facet | Yu, Deshui Wang, Yizhi Jin, Saihui Zhu, Taojie Zeng, Fanyi Qian, Wen Huang, Zirui Ouyang, Jingli Li, Jiameng Song, Zhen Guan, Tian He, Yonghong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08603 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | YpathRAG:A Retrieval-Augmented Generation Framework and Benchmark for Pathology Yu, Deshui Wang, Yizhi Jin, Saihui Zhu, Taojie Zeng, Fanyi Qian, Wen Huang, Zirui Ouyang, Jingli Li, Jiameng Song, Zhen Guan, Tian He, Yonghong Computation and Language 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. |
| title | YpathRAG:A Retrieval-Augmented Generation Framework and Benchmark for Pathology |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.08603 |