Efficient Medical Question Answering with Knowledge-Augmented Question Generation

• URL: http://arxiv.org/abs/2405.14654v1
• Date: Thu, 23 May 2024 14:53:52 GMT
• Title: Efficient Medical Question Answering with Knowledge-Augmented Question Generation
• Authors: Julien Khlaut, Corentin Dancette, Elodie Ferreres, Alaedine Bennani, Paul Hérent, Pierre Manceron,
• Abstract summary: We introduce a method to improve the proficiency of a small language model in the medical domain by employing a two-fold approach. We first fine-tune the model on a corpus of medical textbooks. Then, we use GPT-4 to generate questions similar to the downstream task, prompted with textbook knowledge, and use them to fine-tune the model.
• Score: 5.145812785735094
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the expanding field of language model applications, medical knowledge representation remains a significant challenge due to the specialized nature of the domain. Large language models, such as GPT-4, obtain reasonable scores on medical question answering tasks, but smaller models are far behind. In this work, we introduce a method to improve the proficiency of a small language model in the medical domain by employing a two-fold approach. We first fine-tune the model on a corpus of medical textbooks. Then, we use GPT-4 to generate questions similar to the downstream task, prompted with textbook knowledge, and use them to fine-tune the model. Additionally, we introduce ECN-QA, a novel medical question answering dataset containing ``progressive questions'' composed of related sequential questions. We show the benefits of our training strategy on this dataset. The study's findings highlight the potential of small language models in the medical domain when appropriately fine-tuned. The code and weights are available at https://github.com/raidium-med/MQG.

Related papers

• How do you know that? Teaching Generative Language Models to Reference Answers to Biomedical Questions [0.0]
  Large language models (LLMs) have recently become the leading source of answers for users' questions online. Despite their ability to offer eloquent answers, their accuracy and reliability can pose a significant challenge. This paper introduces a biomedical retrieval-augmented generation (RAG) system designed to enhance the reliability of generated responses.
  arXiv  Detail & Related papers  (2024-07-06T09:10:05Z)
• Medical Vision-Language Pre-Training for Brain Abnormalities [96.1408455065347]
  We show how to automatically collect medical image-text aligned data for pretraining from public resources such as PubMed. In particular, we present a pipeline that streamlines the pre-training process by initially collecting a large brain image-text dataset. We also investigate the unique challenge of mapping subfigures to subcaptions in the medical domain.
  arXiv  Detail & Related papers  (2024-04-27T05:03:42Z)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models [49.95603725998561]
  We propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts. Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model.
  arXiv  Detail & Related papers  (2023-10-04T21:57:09Z)
• Visual Question Answering in the Medical Domain [13.673890873313354]
  We present a novel contrastive learning pretraining method to mitigate the problem of small datasets for the Med-VQA task. Our proposed model obtained an accuracy of 60% on the VQA-Med 2019 test set, giving comparable results to other state-of-the-art Med-VQA models.
  arXiv  Detail & Related papers  (2023-09-20T06:06:10Z)
• Med-Flamingo: a Multimodal Medical Few-shot Learner [58.85676013818811]
  We propose Med-Flamingo, a multimodal few-shot learner adapted to the medical domain. Based on OpenFlamingo-9B, we continue pre-training on paired and interleaved medical image-text data from publications and textbooks. We conduct the first human evaluation for generative medical VQA where physicians review the problems and blinded generations in an interactive app.
  arXiv  Detail & Related papers  (2023-07-27T20:36:02Z)
• PMC-LLaMA: Towards Building Open-source Language Models for Medicine [62.39105735933138]
  Large Language Models (LLMs) have showcased remarkable capabilities in natural language understanding. LLMs struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. We describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA.
  arXiv  Detail & Related papers  (2023-04-27T18:29:05Z)
• ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development [1.4315915057750197]
  We publish a Vietnamese dataset of medical questions from patients with sentence-level and entity-level annotations. We propose a simple self-supervised training strategy with span-noise modelling that improves the performance.
  arXiv  Detail & Related papers  (2023-04-27T17:59:53Z)
• Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models [42.360431316298204]
  We focus on open-ended VQA and motivated by the recent advances in language models consider it as a generative task. To properly communicate the medical images to the language model, we develop a network that maps the extracted visual features to a set of learnable tokens. We evaluate our approach on the prime medical VQA benchmarks, namely, Slake, OVQA and PathVQA.
  arXiv  Detail & Related papers  (2023-03-10T15:17:22Z)
• Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question Answering [5.294803923794887]
  We propose a contextual method that combines open-domain model aoa and biobert model pre-trained on biomedical domain data. We adopt unsupervised pre-training on large biomedical corpus and supervised fine-tuning on biomedical question answering. Experimental results show that our model outperforms state-of-the-art system by a large margin.
  arXiv  Detail & Related papers  (2022-06-26T12:47:38Z)
• 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.