JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability

• URL: http://arxiv.org/abs/2402.17887v4
• Date: Fri, 28 Jun 2024 13:23:31 GMT
• Title: JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability
• Authors: Junda Wang, Zhichao Yang, Zonghai Yao, Hong Yu,
• Abstract summary: Large Language Models (LLMs) have demonstrated a remarkable potential in medical knowledge acquisition and question-answering. LLMs can potentially hallucinate and yield factually incorrect outcomes, even with domain-specific pretraining. We introduce JMLR (for Jointly trains LLM and information Retrieval) during the fine-tuning phase to address hallucinations.
• Score: 8.476124605775976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated a remarkable potential in medical knowledge acquisition and question-answering. However, LLMs can potentially hallucinate and yield factually incorrect outcomes, even with domain-specific pretraining. Previously, retrieval augmented generation (RAG) has limited success in addressing hallucinations. Unlike previous methods in RAG where the retrieval model was trained separately from the LLM, we introduce JMLR (for Jointly trains LLM and information Retrieval) during the fine-tuning phase. The synchronized training mechanism enhances JMLR's ability to retrieve clinical guidelines and leverage medical knowledge to reason and answer questions and reduces the demand for computational resources. We evaluated JMLR on the important medical question-answering application. Our experimental results demonstrate that JMLR-13B (70.5%) outperforms a previous state-of-the-art open-source model using conventional pre-training and fine-tuning Meditron-70B (68.9%) and Llama2-13B with RAG (67.7%) on a medical question-answering dataset. Comprehensive evaluations reveal JMLR-13B enhances reasoning quality and reduces hallucinations better than Claude3-Opus. Additionally, JMLR-13B (148 GPU hours) also trains much faster than Meditron-70B (42630 GPU hours). Through this work, we provide a new and efficient knowledge enhancement method for healthcare, demonstrating the potential of integrating retrieval and LLM training for medical question-answering systems.

Related papers

• Enhancing Biomedical Knowledge Retrieval-Augmented Generation with Self-Rewarding Tree Search and Proximal Policy Optimization [50.26966969163348]
  Large Language Models (LLMs) have shown great potential in the biomedical domain with the advancement of retrieval-augmented generation (RAG) Existing retrieval-augmented approaches face challenges in addressing diverse queries and documents, particularly for medical knowledge queries. We propose Self-Rewarding Tree Search (SeRTS) based on Monte Carlo Tree Search (MCTS) and a self-rewarding paradigm.
  arXiv  Detail & Related papers  (2024-06-17T06:48:31Z)
• MedExQA: Medical Question Answering Benchmark with Multiple Explanations [2.2246416434538308]
  This paper introduces MedExQA, a novel benchmark in medical question-answering to evaluate large language models' (LLMs) understanding of medical knowledge through explanations. By constructing datasets across five distinct medical specialties, we address a major gap in current medical QA benchmarks. Our work highlights the importance of explainability in medical LLMs, proposes an effective methodology for evaluating models beyond classification accuracy, and sheds light on one specific domain, speech language pathology.
  arXiv  Detail & Related papers  (2024-06-10T14:47:04Z)
• Large Language Model Distilling Medication Recommendation Model [61.89754499292561]
  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)
• MEDITRON-70B: Scaling Medical Pretraining for Large Language Models [91.25119823784705]
  Large language models (LLMs) can potentially democratize access to medical knowledge. We release MEDITRON: a suite of open-source LLMs with 7B and 70B parameters adapted to the medical domain.
  arXiv  Detail & Related papers  (2023-11-27T18:49:43Z)
• Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model [41.11769935795965]
  We present a multi-stage training method combining Domain-specific Continued Pre-training (DCPT), Supervised Fine-tuning (SFT), and Direct Preference Optimization (DPO) In the CPT and SFT phases, Qilin-Med achieved 38.4% and 40.0% accuracy on the CMExam test set, respectively. In the DPO phase, it scored 16.66 in BLEU-1 and 27.44 in ROUGE-1 on the Huatuo-26M test set, bringing further improvement to the SFT phase (12.69 in BLEU-1 and 24.21 in ROUGE-1)
  arXiv  Detail & Related papers  (2023-10-13T13:17:03Z)
• MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering [42.528771319248214]
  Large Language Models (LLMs) often perform poorly on domain-specific tasks like medical question answering (QA) We propose a comprehensive retrieval strategy to extract medical facts from an external knowledge base, and then inject them into the query prompt for LLMs. Our retrieval-augmented Vicuna-7B model exhibited an accuracy improvement from 44.46% to 48.54%.
  arXiv  Detail & Related papers  (2023-09-27T21:26:03Z)
• Augmenting Black-box LLMs with Medical Textbooks for Clinical Question Answering [54.13933019557655]
  We present a system called LLMs Augmented with Medical Textbooks (LLM-AMT) LLM-AMT integrates authoritative medical textbooks into the LLMs' framework using plug-and-play modules. We found that medical textbooks as a retrieval corpus is proven to be a more effective knowledge database than Wikipedia in the medical domain.
  arXiv  Detail & Related papers  (2023-09-05T13:39:38Z)
• MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records [60.35217378132709]
  Large language models (LLMs) can follow natural language instructions with human-level fluency. evaluating LLMs on realistic text generation tasks for healthcare remains challenging. We introduce MedAlign, a benchmark dataset of 983 natural language instructions for EHR data.
  arXiv  Detail & Related papers  (2023-08-27T12:24:39Z)

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.