Enhancing the Traditional Chinese Medicine Capabilities of Large Language Model through Reinforcement Learning from AI Feedback
- URL: http://arxiv.org/abs/2411.00897v1
- Date: Fri, 01 Nov 2024 04:19:55 GMT
- Title: Enhancing the Traditional Chinese Medicine Capabilities of Large Language Model through Reinforcement Learning from AI Feedback
- Authors: Song Yu, Xiaofei Xu, Fangfei Xu, Li Li,
- Abstract summary: We propose a framework to improve the performance of large language models for Traditional Chinese Medicine (TCM) tasks using only a small amount of data.
We use medical case data for supervised fine-tuning of the large model, making it initially capable of performing TCM tasks.
We further optimize the model's performance using reinforcement learning from AI feedback (RLAIF) to align it with the preference data.
- Score: 5.855520522078306
- License:
- Abstract: Although large language models perform well in understanding and responding to user intent, their performance in specialized domains such as Traditional Chinese Medicine (TCM) remains limited due to lack of expertise. In addition, high-quality data related to TCM is scarce and difficult to obtain, making large language models ineffective in handling TCM tasks. In this work, we propose a framework to improve the performance of large language models for TCM tasks using only a small amount of data. First, we use medical case data for supervised fine-tuning of the large model, making it initially capable of performing TCM tasks. Subsequently, we further optimize the model's performance using reinforcement learning from AI feedback (RLAIF) to align it with the preference data. The ablation study also demonstrated the performance gain is attributed to both supervised fine-tuning and the direct policy optimization. The experimental results show that the model trained with a small amount of data achieves a significant performance improvement on a representative TCM task.
Related papers
- CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning [4.004641316826348]
We introduce a novel language-image Contrastive Learning method with an Efficient large language model and prompt Fine-Tuning (CLEFT)
Our method demonstrates state-of-the-art performance on multiple chest X-ray and mammography datasets.
The proposed parameter efficient framework can reduce the total trainable model size by 39% and reduce the trainable language model to only 4% compared with the current BERT encoder.
arXiv Detail & Related papers (2024-07-30T17:57:32Z) - CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare [12.218718086529462]
This study focuses on the Comprehensive Medical Benchmark in Chinese (CMB)
We successfully trained a smaller base model to achieve scores comparable to larger models.
By integrating a wide range of instructional content, our approach addresses potential issues such as data quality inconsistencies.
arXiv Detail & Related papers (2024-07-29T05:00:48Z) - Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning [13.964106147449051]
Existing solutions concentrate on fine-tuning the pre-trained models on conventional image datasets.
We propose a novel and effective framework based on learning Visual Prompts (VPT) in the pre-trained Vision Transformers (ViT)
We demonstrate that our new approximations with semantic information are superior to representative capabilities.
arXiv Detail & Related papers (2024-02-04T04:42:05Z) - TCM-GPT: Efficient Pre-training of Large Language Models for Domain
Adaptation in Traditional Chinese Medicine [11.537289359051975]
We propose a novel TCMDA (TCM Domain Adaptation) approach, efficient pre-training with domain-specific corpus.
Specifically, we first construct a large TCM-specific corpus, TCM-Corpus-1B, by identifying domain keywords and retreving from general corpus.
Then, our TCMDA leverages the LoRA which freezes the pretrained model's weights and uses rank decomposition matrices to efficiently train specific dense layers for pre-training and fine-tuning.
arXiv Detail & Related papers (2023-11-03T08:54:50Z) - Improving Small Language Models on PubMedQA via Generative Data
Augmentation [4.96649519549027]
Large Language Models (LLMs) have made remarkable advancements in the field of natural language processing.
Small Language Models (SLMs) are known for their efficiency, but they often struggle with limited capacity and training data.
We introduce a novel method aimed at improving SLMs in the medical domain using LLM-based generative data augmentation.
arXiv Detail & Related papers (2023-05-12T23:49:23Z) - Competence-based Multimodal Curriculum Learning for Medical Report
Generation [98.10763792453925]
We propose a Competence-based Multimodal Curriculum Learning framework ( CMCL) to alleviate the data bias and make best use of available data.
Specifically, CMCL simulates the learning process of radiologists and optimize the model in a step by step manner.
Experiments on the public IU-Xray and MIMIC-CXR datasets show that CMCL can be incorporated into existing models to improve their performance.
arXiv Detail & Related papers (2022-06-24T08:16:01Z) - Improving Pre-trained Language Model Fine-tuning with Noise Stability
Regularization [94.4409074435894]
We propose a novel and effective fine-tuning framework, named Layerwise Noise Stability Regularization (LNSR)
Specifically, we propose to inject the standard Gaussian noise and regularize hidden representations of the fine-tuned model.
We demonstrate the advantages of the proposed method over other state-of-the-art algorithms including L2-SP, Mixout and SMART.
arXiv Detail & Related papers (2022-06-12T04:42:49Z) - Visualizing the Relationship Between Encoded Linguistic Information and
Task Performance [53.223789395577796]
We study the dynamic relationship between the encoded linguistic information and task performance from the viewpoint of Pareto Optimality.
We conduct experiments on two popular NLP tasks, i.e., machine translation and language modeling, and investigate the relationship between several kinds of linguistic information and task performances.
Our empirical findings suggest that some syntactic information is helpful for NLP tasks whereas encoding more syntactic information does not necessarily lead to better performance.
arXiv Detail & Related papers (2022-03-29T19:03:10Z) - Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language
Transfer Learning [59.38343286807997]
We propose Model-Agnostic Multitask Fine-tuning (MAMF) for vision-language models on unseen tasks.
Compared with model-agnostic meta-learning (MAML), MAMF discards the bi-level optimization and uses only first-order gradients.
We show that MAMF consistently outperforms the classical fine-tuning method for few-shot transfer learning on five benchmark datasets.
arXiv Detail & Related papers (2022-03-09T17:26:53Z) - Feeding What You Need by Understanding What You Learned [54.400455868448695]
Machine Reading (MRC) reveals the ability to understand a given text passage and answer questions based on it.
Existing research works in MRC rely heavily on large-size models and corpus to improve the performance evaluated by metrics such as Exact Match.
We argue that a deep understanding of model capabilities and data properties can help us feed a model with appropriate training data.
arXiv Detail & Related papers (2022-03-05T14:15:59Z) - Improving Classifier Training Efficiency for Automatic Cyberbullying
Detection with Feature Density [58.64907136562178]
We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods.
We hypothesise that estimating dataset complexity allows for the reduction of the number of required experiments.
The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.
arXiv Detail & Related papers (2021-11-02T15:48:28Z)
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.