Large language models (LLMs) have recently become the leading source of answers for users' questions online. Despite their ability to offer eloquent answers, their accuracy and reliability can pose a significant challenge. This is especially true for sensitive domains such as biomedicine, where there is a higher need for factually correct answers. This paper introduces a biomedical retrieval-augmented generation (RAG) system designed to enhance the reliability of generated responses. The system is based on a fine-tuned LLM for the referenced question-answering, where retrieved relevant abstracts from PubMed are passed to LLM's context as input through a prompt. Its output is an answer based on PubMed abstracts, where each statement is referenced accordingly, allowing the users to verify the answer. Our retrieval system achieves an absolute improvement of 23% compared to the PubMed search engine. Based on the manual evaluation on a small sample, our fine-tuned LLM component achieves comparable results to GPT-4 Turbo in referencing relevant abstracts. We make the dataset used to fine-tune the models and the fine-tuned models based on Mistral-7B-instruct-v0.1 and v0.2 publicly available.
HealthContradict: Evaluating Biomedical Knowledge Conflicts in Language Models
Faculty of Medicine, University of Geneva（日内瓦大学医学院）；Department of Biomedical Informatics, Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学 Yong Loo Lin 医学院生物医学信息学系）；Department of Biostatistics & Bioinformatics, Duke University Artificial Intelligence Institute, National University of Singapore（新加坡国立大学人工智能研究所生物统计学与生物信息学系）
AI-assisted Knowledge Discovery in Biomedical Literature to Support Decision-making in Precision Oncology
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院西德尼·金梅尔综合癌症中心）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）
Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）；Duke-NUS Medical School, National University of Singapore, Singapore（新加坡国立大学杜克-新加坡医学部）
TeachBench: A Syllabus-Grounded Framework for Evaluating Teaching Ability in Large Language Models
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（信息处理国家重点实验室，计算机学院，北京大学）；ByteDance BandAI（字节跳动BandAI）；Institute of Automation, Chinese Academy of Sciences（自动化研究所，中国科学院）
From Knowledge Generation to Knowledge Verification: Examining the BioMedical Generative Capabilities of ChatGPT
MGEN -- College of Engineering, Northeastern University Miami（东北大学迈阿密分校MGEN工程学院）；University of Vermont（佛蒙特大学）；AGH, University of Krakow（克拉科夫AGH大学）；Howard University（霍华德大学）
Exploring Semi-supervised Variational Autoencoders for Biomedical Relation Extraction
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；Dalian University of Technology（大连理工大学）
Investigation of BERT Model on Biomedical Relation Extraction Based on Revised Fine-tuning Mechanism
Department of Computer and Information Science University of Delaware（特拉华大学计算机与信息科学系）；University of Delaware（特拉华大学）；Department of Computer and Information Science（计算机与信息科学系）
MEDAKA: Construction of Biomedical Knowledge Graphs Using Large Language Models
Department of Data Science and its Applications, German Research Center for Artificial Intelligence (DFKI GmbH)（数据科学及其应用系，德国人工智能研究中心（DFKI GmbH））；Department of Computer Science, University of Kaiserslautern–Landau (RPTU)（计算机科学系，凯撒斯劳滕-兰道大学（RPTU））
Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation
Department of Radiation Oncology, Winship Cancer Institute, Emory University School of Medicine（放射肿瘤科，Winship癌症研究所，埃默里大学医学院）；Department of Biomedical Engineering, College of Engineering, Georgia Institute of Technology（生物医学工程系，工程学院，佐治亚理工学院）；Department of Computer Science and Mathematics, Laney Graduate School, Emory University（计算机科学与数学系，兰尼研究生院，埃默里大学）；School of Electrical and Computer Engineering, College of Engineering, Georgia Institute of Technology（电气与计算机工程系，工程学院，佐治亚理工学院）
Building evidence-based knowledge bases from full-text literature for disease-specific biomedical reasoning
School of Computer Science and Technology, Zhejiang University of Science and Technology（浙江理工大学计算机科学与技术学院）；Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College（中国医学科学院药物生物技术研究所）；Zhejiang Key Laboratory of Biomedical Intelligent Computing Technology（浙江省生物医学智能计算技术重点实验室）
Aligning LLMs with Biomedical Knowledge using Balanced Fine-Tuning
Tencent AI for Life Sciences Lab（腾讯AI生命科学实验室）；Sun Yat-sen University（中山大学）；The Hong Kong Polytechnic University（香港理工大学）；Westlake University（西湖大学）；The Hong Kong University of Science and Technology（香港科技大学）
Empowering Language Model with Guided Knowledge Fusion for Biomedical Document Re-ranking
Lister Hill National Center for Biomedical Communications（利斯特·希尔国家生物医学交流中心）；National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）
SLATE: Are AI-Generated Slides Educationally Effective? A Benchmark for Language Teaching Quality and Learner Knowledge Acquisition
Beijing Normal University（北京师范大学）；University of Science and Technology of China（中国科学技术大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Language and Culture University（北京语言大学）；Tsinghua University（清华大学）
KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling
University of Western Ontario（西安大略大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；University of Toronto（多伦多大学）；Unity Health Toronto（多伦多联合健康中心）
KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model
Institute of Artificial Intelligence, Soochow University（苏州大学人工智能研究院）；Peking University（北京大学）；The Hong Kong Polytechnic University（香港理工大学）；Pharmcube（药智魔方）；Changping Laboratory（昌平实验室）
