YpathRAG:A Retrieval-Augmented Generation Framework and Benchmark for Pathology
This addresses factual reliability issues in pathology AI, providing a scalable and interpretable solution for a high-barrier medical domain.
The authors tackled the problem of LLM hallucinations in pathology by developing YpathRAG, a retrieval-augmented generation framework with dual-channel retrieval and evidence judgment, which achieved Recall@5 of 98.64% (a 23 percentage point gain) and increased accuracy on challenging questions by up to 15.6%.
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.