ZhiFangDanTai: Fine-tuning Graph-based Retrieval-Augmented Generation Model for Traditional Chinese Medicine Formula

• URL: http://arxiv.org/abs/2509.05867v1
• Date: Sat, 06 Sep 2025 23:48:46 GMT
• Title: ZhiFangDanTai: Fine-tuning Graph-based Retrieval-Augmented Generation Model for Traditional Chinese Medicine Formula
• Authors: ZiXuan Zhang, Bowen Hao, Yingjie Li, Hongzhi Yin,
• Abstract summary: Traditional Chinese Medicine (TCM) formulas play a significant role in treating epidemics and complex diseases.<n>Existing models for TCM utilize traditional algorithms or deep learning techniques to analyze formula relationships.<n>ZhiFangDanTai is a framework combining Graph-based Retrieval-Augmented Generation (GraphRAG) with Large Language Models fine-tuning.
• Score: 47.414400253177405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional Chinese Medicine (TCM) formulas play a significant role in treating epidemics and complex diseases. Existing models for TCM utilize traditional algorithms or deep learning techniques to analyze formula relationships, yet lack comprehensive results, such as complete formula compositions and detailed explanations. Although recent efforts have used TCM instruction datasets to fine-tune Large Language Models (LLMs) for explainable formula generation, existing datasets lack sufficient details, such as the roles of the formula's sovereign, minister, assistant, courier; efficacy; contraindications; tongue and pulse diagnosis-limiting the depth of model outputs. To address these challenges, we propose ZhiFangDanTai, a framework combining Graph-based Retrieval-Augmented Generation (GraphRAG) with LLM fine-tuning. ZhiFangDanTai uses GraphRAG to retrieve and synthesize structured TCM knowledge into concise summaries, while also constructing an enhanced instruction dataset to improve LLMs' ability to integrate retrieved information. Furthermore, we provide novel theoretical proofs demonstrating that integrating GraphRAG with fine-tuning techniques can reduce generalization error and hallucination rates in the TCM formula task. Experimental results on both collected and clinical datasets demonstrate that ZhiFangDanTai achieves significant improvements over state-of-the-art models. Our model is open-sourced at https://huggingface.co/tczzx6/ZhiFangDanTai1.0.

Related papers

• Knowledge Reasoning of Large Language Models Integrating Graph-Structured Information for Pest and Disease Control in Tobacco [8.03308137619444]
  This paper proposes a large language model (LLM) approach that integrates graph-structured information for knowledge reasoning in tobacco pest and disease control.<n>The proposed method enhances knowledge retrieval and reasoning by explicitly incorporating structured information from a domain-specific knowledge graph.
  arXiv  Detail & Related papers  (2025-12-26T02:48:38Z)
• DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains [13.63225871556018]
  Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs (KGs) by leveraging existing triples and textual information.<n>DrKGC employs a flexible lightweight model training strategy to learn structural embeddings and logical rules within the KG.<n>It then leverages a novel bottom-up graph retrieval method to extract a subgraph for each query guided by the learned rules.
  arXiv  Detail & Related papers  (2025-05-31T20:56:54Z)
• HC-LLM: Historical-Constrained Large Language Models for Radiology Report Generation [89.3260120072177]
  We propose a novel Historical-Constrained Large Language Models (HC-LLM) framework for Radiology report generation.<n>Our approach extracts both time-shared and time-specific features from longitudinal chest X-rays and diagnostic reports to capture disease progression.<n> Notably, our approach performs well even without historical data during testing and can be easily adapted to other multimodal large models.
  arXiv  Detail & Related papers  (2024-12-15T06:04:16Z)
• LLaVA Needs More Knowledge: Retrieval Augmented Natural Language Generation with Knowledge Graph for Explaining Thoracic Pathologies [3.2221734920470797]
  We propose a Vision-Language framework augmented with a Knowledge Graph (KG)-based datastore to generate Natural Language Explanations (NLEs) for medical images.<n>Our framework employs a KG-based retrieval mechanism that not only improves the precision of the generated explanations but also preserves data privacy by avoiding direct data retrieval.<n>These frameworks are validated on the MIMIC-NLE dataset, where they achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2024-10-07T04:59:08Z)
• R2GenCSR: Retrieving Context Samples for Large Language Model based X-ray Medical Report Generation [7.4871243017824165]
  This paper proposes a novel context-guided efficient X-ray medical report generation framework. Specifically, we introduce the Mamba as the vision backbone with linear complexity, and the performance obtained is comparable to that of the strong Transformer model.
  arXiv  Detail & Related papers  (2024-08-19T07:15:11Z)
• Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models [59.89454513692417]
  Tabular data is often hidden in text, particularly in medical diagnostic reports. We propose a novel, simple, and effective methodology for extracting structured tabular data from textual medical reports, called TEMED-LLM. We demonstrate that our approach significantly outperforms state-of-the-art text classification models in medical diagnostics.
  arXiv  Detail & Related papers  (2023-06-08T09:12:28Z)
• An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT [80.33783969507458]
  The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians. Recent studies have achieved promising results in automatic impression generation using large-scale medical text data. These models often require substantial amounts of medical text data and have poor generalization performance.
  arXiv  Detail & Related papers  (2023-04-17T17:13:42Z)
• Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation [92.73584302508907]
  We propose a knowledge graph with Dynamic structure and nodes to facilitate medical report generation with Contrastive Learning. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation.
  arXiv  Detail & Related papers  (2023-03-18T03:53:43Z)
• Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study [62.376800537374024]
  We study scientific LMs for KG completion, exploring whether we can tap into their latent knowledge to enhance biomedical link prediction. We integrate the LM-based models with KG embedding models, using a router method that learns to assign each input example to either type of model and provides a substantial boost in performance.
  arXiv  Detail & Related papers  (2021-06-17T17:55:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.