FineMedLM-o1: Enhancing the Medical Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training

• URL: http://arxiv.org/abs/2501.09213v2
• Date: Thu, 13 Feb 2025 02:44:07 GMT
• Title: FineMedLM-o1: Enhancing the Medical Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training
• Authors: Hongzhou Yu, Tianhao Cheng, Ying Cheng, Rui Feng,
• Abstract summary: FineMedLM-o1 is a large language model for medical reasoning. It uses high-quality synthetic medical data and long-form reasoning data forSupervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) We also introduced Test-Time Training (TTT) in the medical domain for the first time, facilitating domain adaptation and ensuring reliable, accurate reasoning.
• Score: 12.1175788614508
• License:
• Abstract: Recent advancements in large language models (LLMs) have shown promise in medical applications such as disease diagnosis and treatment planning. However, most existing medical LLMs struggle with the advanced reasoning required for complex clinical scenarios, such as differential diagnosis or personalized treatment suggestions. We proposed FineMedLM-o1, which leverages high-quality synthetic medical data and long-form reasoning data for Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), enabling advanced dialogue and deep reasoning capabilities. Additionally, we introduced Test-Time Training (TTT) in the medical domain for the first time, facilitating domain adaptation and ensuring reliable, accurate reasoning. Experimental results demonstrate that FineMedLM-o1 achieves a 23% average performance improvement over prior models on key medical benchmarks. Furthermore, the introduction of TTT provides an additional 14% performance boost, highlighting its effectiveness in enhancing medical reasoning capabilities. To support this process, we also proposed a novel method for synthesizing medical dialogue. Compared to other open-source datasets, our dataset stands out as superior in both quality and complexity. The project and data will be released on GitHub.

Related papers

• IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment [6.022433954095106]
  We introduce a medical instruction dataset, CMedINS, containing six medical instructions derived from actual medical tasks. We then launch our medical model, IIMedGPT, employing an efficient preference alignment method. The results show that our final model outperforms existing medical models in medical dialogue.
  arXiv  Detail & Related papers  (2025-01-06T09:22:36Z)
• MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimization [25.937453082034448]
  We propose MMedPO, a novel multimodal medical preference optimization approach. MMedPO considers the clinical relevance of preference samples to enhance Med-LVLM alignment. Our experiments demonstrate that MMedPO significantly enhances factual accuracy in Med-LVLMs.
  arXiv  Detail & Related papers  (2024-12-09T01:50:39Z)
• RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment [54.91736546490813]
  We introduce the RuleAlign framework, designed to align Large Language Models with specific diagnostic rules. We develop a medical dialogue dataset comprising rule-based communications between patients and physicians. Experimental results demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2024-08-22T17:44:40Z)
• STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering [58.79671189792399]
  STLLaVA-Med is designed to train a policy model capable of auto-generating medical visual instruction data. We validate the efficacy and data efficiency of STLLaVA-Med across three major medical Visual Question Answering (VQA) benchmarks.
  arXiv  Detail & Related papers  (2024-06-28T15:01:23Z)
• Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches [7.3384872719063114]
  We develop and refined a series of medical Large Language Models (LLMs) based on the Llama-2 architecture. Our experiments systematically evaluate the effectiveness of these tuning strategies across various well-known medical benchmarks.
  arXiv  Detail & Related papers  (2024-04-23T06:36:21Z)
• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
  We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
  arXiv  Detail & Related papers  (2024-02-15T06:46:48Z)
• Large Language Model Distilling Medication Recommendation Model [58.94186280631342]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs) Our research aims to transform existing medication recommendation methodologies using LLMs. To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
  arXiv  Detail & Related papers  (2024-02-05T08:25:22Z)
• Integrating Physician Diagnostic Logic into Large Language Models: Preference Learning from Process Feedback [19.564416963801268]
  We propose an approach called preference learning from process feedback. PLPF integrates the doctor's diagnostic logic into LLMs. We show that PLPF enhances the diagnostic accuracy of the baseline model in medical conversations by 17.6%.
  arXiv  Detail & Related papers  (2024-01-11T06:42:45Z)
• ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences [51.66185471742271]
  We propose ChiMed-GPT, a benchmark LLM designed explicitly for Chinese medical domain. ChiMed-GPT undergoes a comprehensive training regime with pre-training, SFT, and RLHF. We analyze possible biases through prompting ChiMed-GPT to perform attitude scales regarding discrimination of patients.
  arXiv  Detail & Related papers  (2023-11-10T12:25:32Z)
• Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching [49.78442796596806]
  We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM) Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
  arXiv  Detail & Related papers  (2023-03-24T03:14:00Z)

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.