Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications
Authors:
Open Access
- 1 January 2025
- conference paper
- Published by Association for Computational Linguistics (ACL)
- p. 15285-15309
Abstract
Zhe Chen, Yusheng Liao, Shuyang Jiang, Pingjie Wang, YiQiu Guo, Yanfeng Wang, Yu Wang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.Keywords
This publication has 40 references indexed in Scilit:
- Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented GenerationIEEE Transactions on Audio, Speech and Language Processing, 2025
- Towards evaluating and building versatile large language models for medicinenpj Digital Medicine, 2025
- Toward expert-level medical question answering with large language modelsNature Medicine, 2025
- Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up QuestionsBiocomputing 2020, 2024
- A Multi-Source Retrieval Question Answering Framework Based on RAG2024
- PMC-LLaMA: toward building open-source language models for medicineJournal of the American Medical Informatics Association, 2024
- MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrievalBioinformatics, 2023
- DialogMCF: Multimodal Context Flow for Audio Visual Scene-Aware DialogIEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023
- MedRetriever2021
- DrugBank: a knowledgebase for drugs, drug actions and drug targetsNucleic Acids Research, 2007