mlsanityData to 7 Oct 2026

Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in Chinese

arXiv 2309.04175 ↗First posted 8 Sep 2023cs.CL cs.AIcited by 18

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Abstract (arXiv)

Large Language Models (LLMs) have demonstrated remarkable success in diverse natural language processing (NLP) tasks in general domains. However, LLMs sometimes generate responses with the hallucination about medical facts due to limited domain knowledge. Such shortcomings pose potential risks in the utilization of LLMs within medical contexts. To address this challenge, we propose knowledge-tuning, which leverages structured medical knowledge bases for the LLMs to grasp domain knowledge efficiently and facilitate reliable response generation. We also release cMedKnowQA, a Chinese medical knowledge question-answering dataset constructed from medical knowledge bases to assess the medical knowledge proficiency of LLMs. Experimental results show that the LLMs which are knowledge-tuned with cMedKnowQA, can exhibit higher levels of accuracy in response generation compared with vanilla instruction-tuning and offer a new reliable way for the domain adaptation of LLMs.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 34 of its 49 references that are in the map. First posted on arXiv 8 Sep 2023.

Builds on

34 of 49 references.

References · 34

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 6; 2026: 4.

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  2. 2024MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learningcited by 63
  3. 2023Making Large Language Models Perform Better in Knowledge Graph Completioncited by 48
  4. 2025Baichuan-M1: Pushing the Medical Capability of Large Language Modelscited by 41
  5. 2024Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosiscited by 23
  6. 2023From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discoverycited by 11
  7. 2024Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuningcited by 10
  8. 2025Fleming-R1: Toward Expert-Level Medical Reasoning via Reinforcement Learningcited by 9
  9. 2024Fine-Tuning Medical Language Models for Enhanced Long-Contextual Understanding and Domain Expertisecited by 4
  10. 2025Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systemscited by 3
  11. 2025A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucinationcited by 2
  12. 2023Make Your Decision Convincing! A Unified Two-Stage Framework: Self-Attribution and Decision-Makingcited by 1
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  1. 2025C-PATH: Conversational Patient Assistance and Triage in Healthcare Systemcited by 1
  2. 2025Uncovering the Role of Initial Saliency in U-Shaped Attention Bias: Scaling Initial Token Weight for Enhanced Long-Text Processingcited by 1
  3. 2026TCM-DiffRAG: Personalized Syndrome Differentiation Reasoning Method for Traditional Chinese Medicine based on Knowledge Graph and Chain of Thoughtcited by 0
  4. 2026CORE-Acu: Structured Reasoning Traces and Knowledge Graph Safety Verification for Acupuncture Clinical Decision Supportcited by 0
  5. 2026Evidence-Based Intelligent Diagnostic and Therapeutic Visualization System with Large Language Models: Multi-Turn Interaction and Multimodal Treatment Plan Generationcited by 0
  6. 2026Didactic knowledge or Clinical Cases? How Data Types Shape Medical Large Language Modelscited by 0