Biomedical QA

QA: Question Answering

Follow this topic Ask about this topic

Momentum

2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 30

All topics

SAGE: Semantic Anchor-Guided Evolution for Grounded Medical QA Data Synthesis
Oct 6, 2026Chuan Li, Chengyu Wang, Cen Chen +3Synthetic Data Generation Biomedical QA
East China Normal University, Shanghai, China · Alibaba Group, Hangzhou, China
Overview of BioASQ 2026: The fourteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
Sep 30, 2026Anastasios Nentidis, Georgios Katsimpras, Anastasia Krithara +18Biomedical QA Clinical Information Extraction
National Center for Scientific Research “Demokritos”, Athens, Greece · Barcelona Supercomputing Center, Barcelona, Spain · Lomonosov Moscow State University, Russia +6
Recursive LLM Degradation in Biomedical Question Answering: A Cross-Generation Study
Sep 28, 2026Bibek Bhandari, Kshitij LingthepBiomedical QA Synthetic Data Generation for Language Models
Independent Researcher Memphis, Tennessee, USA
AdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA
Aug 31, 2026Jun Hyeong Kim, Dongki Kim, Yinhua Piao +1Multi-Hop QA Biomedical QA
KAIST
Research Assistant: AstraZeneca's Agentic System for R&D
Aug 6, 2026Piotr Grabowski, Mohamed Alameen, Jorge Bretones +16AI-Assisted Scientific Research Biomedical QA
AstraZeneca, RDIT, Biological Insights Knowledge Graph
Retrieval Augmented Biomedical Question Answering with Weak Question Recovery and Neural Reranking for BioASQ Task 14b
Aug 2, 2026Xueying Zhao, Lee Mai, Balaji AnandganeshRetrieval-Augmented Generation Learning to Rank
Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332
Cost-Pragmatic Quality Gating and Selection-Fusion Multi-Model Combiners for BioASQ Phases A+ and B
Jul 15, 2026Dima Galat, Marian-Andrei RizoiuRetrieval-Augmented Generation Biomedical QA
University of Technology Sydney, Australia
FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis
Jul 13, 2026Marlena Flüh, Soo-Yon Kim, Carolin Victoria Schneider +1KG Construction Graph Retrieval-Augmented Generation
Data Stream Management and Analysis RWTH Aachen University Aachen, Germany · Gastroenterology, Metabolic Diseases and Intensive Care University Hospital RWTH Aachen Aachen, Germany
Capabilities of Claude Fable 5 on Biomedical Challenge Problems
Jul 12, 2026Dominic Okonkwo, Magnus Hodgson, Temitope I. David +1Biomedical QA LLM Refusal Behavior
School of Computing, University of Georgia · Department of Chemistry, University of Illinois · Institute of Bioinformatics, University of Georgia
From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b
Jul 7, 2026Taeyun Roh, Eunha Lee, Wonjune Jang +3LLM Answer Verification Biomedical QA
Department of Computer Science and Engineering, Korea University, Seoul, 02841, Republic of Korea · Department of Mathematics, Myongji University, Yongin, 17058, Republic of Korea · 3AIGEN Sciences, Seoul, 04778, Republic of Korea
Hybrid-IR: Dual-Path Hybrid Retrieval with Iterative Reasoning for Complex Medical Question Answering
Jun 24, 2026Sheng Wan, Jiahui Zhang, Zicheng Zhao +1Retrieval-Augmented Generation Graph Retrieval-Augmented Generation
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China · School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
OpenBioRQ: Unsolved Biomedical Research Questions for Agents
Jun 20, 2026Minbyul JeongAI Agent Reliability Citation Verification
Upstage AI
BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discovery
Jun 19, 2026Jieyi Wang, Bingxuan Li, Nanyi Jiang +9AI for Science Multi-Agent Orchestration
1Peking University · University of Illinois at Urbana-Champaign · University of Pennsylvania +2
WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning
Jun 16, 2026Yuwei Zhang, Tong Xia, Bianca Emmerich +5Biomedical QA Wearable Sensing
University of Cambridge · Tsinghua University · University College London +2
Weaving Multi-Source Evidence for Biomedical Reasoning: The BioMedHop Benchmark and BioWeave Framework
Jun 15, 2026Xingyu Tan, Shiyuan Liu, Xiaoyang Wang +5Multi-Hop QA Evidence-Grounded Reasoning
University of New South Wales, Australia · 2CSIRO, Australia · University of Technology Sydney, Australia
When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG
Jun 2, 2026Erfan Nourbakhsh, Rocky Slavin, Ke Yang +1Retrieval-Augmented Generation LLM Grounding
The University of Texas at San Antonio
DrugClaw and DrugAudit: A Primary-Source-Grounded Agent and Authority-Aware Benchmark for Drug-Information Question Answering
May 31, 2026Qing Wang, Bo Li, Jialu Liang +3Biomedical QA Agentic RAG
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Florida, USA · PAMI Research Group, Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau, China
UniD $^3$ : A Knowledge Graph-Enhanced RAG Framework for Drug-Disease Discovery and Reasoning
May 31, 2026Qing Wang, Tianshi Liu, Minghao Zhou +5Relation Extraction KG Construction
Department of Health Outcomes and Biomedical Informatics, University of Florida, Florida, USA · Department of Hematology, H. Lee Moffitt Cancer Center and Research Institute, Florida, USA · Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA +2
HypothesisMed: Inference-Time Answer Fusion and Structured Hypothesis-Space Reporting for Biomedical Question Answering
May 31, 2026Md Motaleb Hossen Manik, Ge WangBiomedical QA LLM Uncertainty Estimation
Department of Computer Science Rensselaer Polytechnic Institute Troy, NY, USA · Department of Biomedical Engineering Rensselaer Polytechnic Institute Troy, NY, USA
ChronoMedKG: A Temporally-Grounded Biomedical Knowledge Graph and Benchmark for Clinical Reasoning
May 21, 2026Md Shamim Ahmed, Farzaneh Firoozbakht, Lukas Galke Poech +2KG Construction Healthcare
University of Southern Denmark · University of Hamburg
BELIEF: Structured Evidence Modeling and Uncertainty-Aware Fusion for Biomedical Question Answering
May 17, 2026Chang Zong, Hao Ning, Siliang Tang +2Healthcare Evidence-Grounded Reasoning
When Evidence Conflicts: Uncertainty and Order Effects in Retrieval-Augmented Biomedical Question Answering
May 13, 2026Yikun Han, Mengfei Lan, Halil KilicogluSelective Prediction Healthcare
University of Illinois Urbana-Champaign
MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering
May 12, 2026Rezarta Islamaj, Robert Leaman, Joey Chan +13Multi-Hop QA LLM Evaluation
National Library of Medicine, Division of Intramural Research, Bethesda, MD, US · University of Illinois at Urbana-Champaign, Department of Computer Science, Urbana, IL, US · University of Michigan Medical School, Ann Arbor, Michigan, US
Overview of the MedHopQA track at BioCreative IX: track description, participation and evaluation of systems for multi-hop medical question answering
May 12, 2026Rezarta Islamaj, Joey Chan, Robert Leaman +13Multi-Hop QA Healthcare
National Library of Medicine (NLM), National Institutes of Health (NIH), MD, 20894, Bethesda, USA · University of Illinois at Urbana Champaign · Korea University +7
Evaluating Advanced Prompting on Gemini Flash for Multi-Hop Biomedical QA
May 5, 2026Ahmed Bajaber, Mohammed AlliheediMulti-Hop QA LLM Evaluation
1Saudi Med AI Lab (SMAIL) 2Prince Sultan University, Riyadh, Saudi Arabia · 2Prince Sultan University, Riyadh, Saudi Arabia · 3Al-Baha University, Al Baha, Saudi Arabia
Benchmarking Retrieval Strategies for Biomedical Retrieval-Augmented Generation: A Controlled Empirical Study
May 4, 2026Devi Prasad Bal, Subhashree PuhanCross-Encoder Reranking Retrieval-Augmented Generation
The University of Texas at Austin
BioGraphletQA: Knowledge-Anchored Generation of Complex QA Datasets
Apr 28, 2026Richard A. A. Jonker, Bárbara Maria Ribeiro de Abreu Martins, Sérgio MatosBiomedical QA Synthetic Benchmark Generation
IEETA, DETI, LASI, University of Aveiro, Aveiro, Portugal · USF Atlântico Norte, Portugal
If Only My CGM Could Speak: A Privacy-Preserving Agent for Question Answering over Continuous Glucose Data
Apr 18, 2026Yanjun Cui, Ali Emami, Temiloluwa Prioleau +1Privacy-Preserving Language Models Healthcare
Dartmouth College · Emory University
DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI
Apr 16, 2026Zhizheng Wang, Chih-Hsuan Wei, Joey Chan +19Deep Research Agents Healthcare
Division of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20892, USA · Hunterian Neurosurgical Laboratory, Johns Hopkins School of Medicine, Baltimore, MD 21231, USA · Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892, USA +7
EpiQAL: Benchmarking Large Language Models in Epidemiological Question Answering and Reasoning
Jan 6, 2026Mingyang Wei, Dehai Min, Zewen Liu +8LLM Evaluation Healthcare
1Emory University · University of Illinois Chicago · 3Microsoft

Oct 6, 2026, cs.CLJ/K move · Enter open · S save

SAGE: Semantic Anchor-Guided Evolution for Grounded Medical QA Data Synthesis

Chuan Li, Chengyu Wang, Cen Chen, Ye Lyu+2

East China Normal University, Shanghai, China · Alibaba Group, Hangzhou, China

Synthetic Data Generation Biomedical QA Synthetic Data Generation for Language Models Medical LLMs

Open paper AskPDF arXiv

Developing reliable models for clinical tasks, such as Medical Question Answering (QA), is severely constrained by the limited availability of high-quality, expert-annotated training data. This challenge is exacerbated by stringent privacy requirements and the impracticality of utilizing large open-source corpora or proprietary cloud APIs within resource-limited clinical settings. To address these obstacles, we introduce SAGE (\textit{Semantic Anchor-Guided Evolution}), a novel data synthesis framework that enables small, locally deployed models to generate high-quality medical training data. SAGE leverages lightweight, publicly available taxonomies such as MeSH as semantic anchors, imposing a structured prior to effectively guide and ground the data generation process. At its core, SAGE iteratively interleaves atomic (individual concept-based) and associative (relation-based) synthesis, bootstrapping training data from minimal seeds. This approach eliminates the need for large collections of medical documents or reliance on external APIs, providing a practical solution for on-premises data creation. Extensive experiments across multiple medical question-answering benchmarks demonstrate that models fine-tuned with SAGE-synthesized data consistently outperform those trained using self-derived or conventional document-based paradigms, highlighting tangible improvements in data efficiency and resource utilization for medical LLM development. Code is available at https://github.com/DIaacKr/SAGE.

Momentum

Related topics

Latest papers 30