Information Retrieval (IR) is the task of obtaining pieces of data (such as documents) that are relevant to a particular query or need from a large repository of information. IR is a valuable component of several downstream Natural Language Processing (NLP) tasks. Practically, IR is at the heart of many widely-used technologies like search engines. While probabilistic ranking functions like the Okapi BM25 function have been utilized in IR systems since the 1970's, modern neural approaches pose certain advantages compared to their classical counterparts. In particular, the release of BERT (Bidirectional Encoder Representations from Transformers) has had a significant impact in the NLP community by demonstrating how the use of a Masked Language Model trained on a large corpus of data can improve a variety of downstream NLP tasks, including sentence classification and passage re-ranking. IR Systems are also important in the biomedical and clinical domains. Given the increasing amount of scientific literature across biomedical domain, the ability find answers to specific clinical queries from a repository of millions of articles is a matter of practical value to medical professionals. Moreover, there are domain-specific challenges present, including handling clinical jargon and evaluating the similarity or relatedness of various medical symptoms when determining the relevance between a query and a sentence. This work presents contributions to several aspects of the Biomedical Semantic Information Retrieval domain. First, it introduces Multi-Perspective Sentence Relevance, a novel methodology of utilizing BERT-based models for contextual IR. The system is evaluated using the BioASQ Biomedical IR Challenge. Finally, practical contributions in the form of a live IR system for medics and a proposed challenge on the Living Systematic Review clinical task are provided.
RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering
Department of CSE Emory University Georgia, USA（计算机科学与工程系 埃默里大学 美国佐治亚州）；Department of CSE SRM University AP Andhra Pradesh, India（计算机科学与工程系 SRM大学AP 安得拉邦印度）；Department of CSE Trine University Michigan, USA（计算机科学与工程系 特林大学 密歇根州美国）
MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval
National Center for Biotechnology Information (NCBI)（国家生物技术信息中心）；National Library of Medicine (NLM)（国家医学图书馆）；National Institutes of Health (NIH)（国立卫生研究院）
MedCPT 利用 2.55 亿条 PubMed 点击日志进行对比预训练，首次实现生物医学零样本语义检索，并在六个 IR 任务上超越 GPT-3 规模基线模型。
BioMedSearch: A Multi-Source Biomedical Retrieval Framework Based on LLMs
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Johns Hopkins University（约翰霍普金斯大学）；Henan University（河南大学）
Training-Free BioNEL-RAG: Advancing Biomedical Entity Linking through Retrieval-Augmented Generation and Large Language Model
University of Science and Technology of China（中国科学技术大学）；School of Biomedical Engineering（生物医学工程学院）；Division of Life Sciences and Medicine（生命科学与医学部）；Department of Precision Machinery and Precision Instruments（精密机械与精密仪器系）
SSE-Bio: A Structured Self-Evolving Agent with Agentic Retrieval Policy for Multi-Hop Biomedical Reasoning
School of Computing Science, University of Glasgow（格拉斯哥大学计算科学学院）；Brigham and Women’s Hospital, Harvard Medical School, Harvard University（哈佛大学医学院附属布莱根妇女医院）；Language Technology Lab, University of Cambridge（剑桥大学语言技术实验室）；School of Natural and Computing Science, University of Aberdeen（阿伯丁大学自然与计算科学学院）；School of Cancer Sciences, University of Glasgow（格拉斯哥大学癌症科学学院）；Cancer Research UK Scotland Institute（英国癌症研究苏格兰研究所）
Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models
Department of Biomedical Informatics, School of Medicine, Emory University（生物医学信息学系，医学院，埃默里大学）；Department of Computer Science, Emory University（计算机科学系，埃默里大学）；National Library of Medicine, National Institutes of Health（国家医学图书馆，国立卫生研究院）；Department of Biomedical Engineering, Georgia Institute of Technology and Emory University（生物医学工程系，佐治亚理工学院和埃默里大学）
Exploration of Augmentation Strategies in Multi-modal Retrieval-Augmented Generation for the Biomedical Domain: A Case Study Evaluating Question Answering in Glycobiology
University of Maribor, Faculty of Health Sciences（莫拉维亚大学健康科学学院）；University of Ljubljana, Medical Factory（卢布尔雅那大学医疗工厂）；Genos Ltd（基因公司）；Center for Smart Health, School of Nursing The Hong Kong Polytechnic University（智能健康中心护理学院香港理工大学）；University of Zagreb, Faculty of Pharmacy（扎格雷布大学药学院）；Usher Institute University of Edinburgh（埃德蒙顿大学usher研究所）
DeepRAG: Integrating Hierarchical Reasoning and Process Supervision for Biomedical Multi-Hop QA
Department of Information Science, University of Pittsburgh（信息科学系，匹兹堡大学）；Department of Biomedical Informatics, University of Pittsburgh（生物医学信息学系，匹兹堡大学）；Department of Health Information Management and ISP, University of Pittsburgh（健康信息管理与信息科学系，匹兹堡大学）
On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective
CAS Key Lab of Network Data Science and Technology, ICT, CAS（中国科学院计算技术研究所网络数据科学与技术重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；University of Amsterdam（阿姆斯特丹大学）
Data Augmentation for Biomedical Factoid Question Answering
Athens University of Economics and Business（雅典经济与商业大学）；Institute for Language and Speech Processing, Research Center ‘Athena’（“雅典娜”研究中心语言与语音处理研究所）；Institute of Informatics and Telecommunications, NCSR ‘Demokritos’（国家科学研究中心“德谟克利特”信息与电信研究所）
