mlsanityData to 7 Oct 2026

UETQuintet at BioCreative IX -- MedHopQA: Enhancing Biomedical QA with Selective Multi-hop Reasoning and Contextual Retrieval

arXiv 2601.06974 ↗First posted 11 Jan 2026cs.CLcited by 0

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

Biomedical Question Answering systems play a critical role in processing complex medical queries, yet they often struggle with the intricate nature of medical data and the demand for multi-hop reasoning. In this paper, we propose a model designed to effectively address both direct and sequential questions. While sequential questions are decomposed into a chain of sub-questions to perform reasoning across a chain of steps, direct questions are processed directly to ensure efficiency and minimise processing overhead. Additionally, we leverage multi-source information retrieval and in-context learning to provide rich, relevant context for generating answers. We evaluated our model on the BioCreative IX - MedHopQA Shared Task datasets. Our approach achieves an Exact Match score of 0.84, ranking second on the current leaderboard. These results highlight the model's capability to meet the challenges of Biomedical Question Answering, offering a versatile solution for advancing medical research and practice.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 10 of its 16 references that are in the map. First posted on arXiv 11 Jan 2026.

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  1. 2024Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models27 Jan

Other references · 9

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  3. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
  4. 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoningcited by 177
  5. 2020Unsupervised Question Decomposition for Question Answeringcited by 118
  6. 2021Biomedical Question Answering: A Survey of Approaches and Challengescited by 46
  7. 2023MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answeringcited by 23
  8. 2025SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering?cited by 9
  9. 2024Multi-hop Question Answering over Knowledge Graphs using Large Language Modelscited by 6

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  1. 2026Overview of the MedHopQA track at BioCreative IX: track description, participation and evaluation of systems for multi-hop medical question answering
  2. 2026MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering
  3. 2025RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering
  4. 2025Beyond Retrieval: Ensembling Cross-Encoders and GPT Rerankers with LLMs for Biomedical QA
  5. 2025Harnessing Collective Intelligence of LLMs for Robust Biomedical QA: A Multi-Model Approach
  6. 2026Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering
  7. 2025BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain
  8. 2026PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering