Summary PMC-LLaMA Finetuning LLaMA on Medical Papers arxiv.org
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The PMC-LLaMA model is an open-source language model fine-tuned on biomedical academic papers, achieving high performance on biomedical QA benchmarks and outperforming the original LLaMA model.
Key Points
- The PMC-LLaMA model is a language model fine-tuned for medical tasks by OpenAI using 4.8 million medical papers.
- PMC-LLaMA outperforms the original LLaMA model and achieves competitive results even under zero-shot evaluation.
- The model is fine-tuned using the AdamW optimizer with a learning rate of 2e-5 and a batch size of 128 for 3 epochs.
- The datasets used for training and testing include USMLE, MedMCQA, and PubMedQA.
- Large language models often exhibit unsatisfactory performance in medical applications due to a lack of domain-specific knowledge. PMC-LLaMA addresses this issue by injecting medical knowledge and enhancing its capability in the medical domain.
- Future work includes injecting more domain knowledge into pre-trained models and continuously training the PMC-LLaMA model.
Summaries
277 word summary
PMC-LLaMA is an open-source language model that is fine-tuned on biomedical academic papers to enhance its capability in the medical domain. The model is trained for 5 epochs with 8 A100 GPUs using the Fully Sharded Data Parallel (FSDP) acceleration strategy and bf16 data format. The dataset used is S2ORC Datasets with 81.1M English-language papers, filtered with PubMed Central (PMC)-id. PMC-LLaMA achieves high performance on biomedical QA benchmarks, including PubMedQA, MedMCQA, and USMLE, and can efficiently learn medical knowledge from downstream training data. The authors compare the performance of their modified model, PMC-LLaMA, to the original LLaMA and other language models like ChatGPT and InstructGPT. They show that PMC-LLaMA outperforms LLaMA and achieves competitive results even under zero-shot evaluation. The authors also demonstrate the effectiveness of data-efficient fine-tuning and full fine-tuning on different medical datasets. They conclude that PMC-LLaMA offers better initialization for medical tasks and converges faster than LLaMA. Researchers at OpenAI have created the PMC-LLaMA model by fine-tuning the LLaMA model on 4.8 million medical papers. The PMC-LLaMA model outperforms the original LLaMA model and includes more relevant medical knowledge. However, it has only been trained for five epochs and needs further training. The researchers plan to inject more domain knowledge into pre-trained models and conduct a preliminary investigation by fine-tuning LLaMA. The PMC-LLaMA model is more accurate and informative than the original LLaMA model on medical scenarios like COPD, robotic cardiac surgery, and pneumonia.
The document also cites several resources related to language models and medical research, including the Llama model, Medmcqa dataset, GPT-4 model, Peft method, Semantic Scholar Open Research Corpus, Vicuna chatbot, and other relevant datasets and general resources for medical research.
728 word summary
PMC-LLaMA Finetuning LLaMA on Medical Papers is a document that cites several resources related to language models and medical research. One resource is the Llama model created by Tatsu Lab, which is an instruction-following model that encodes clinical knowledge. Another resource is the Medmcqa dataset, which is a large-scale multi-subject multi-choice dataset for medical domain question answering. Additionally, the document mentions the GPT-4 model, which has capabilities for solving medical challenge problems. Other resources mentioned include Peft, a state-of-the-art parameter-efficient fine-tuning method, and the Semantic Scholar Open Research Corpus. Vicuna is an open-source chatbot that impresses GPT-4 with 90% chat GPT quality. The document also includes references to other relevant datasets and general resources for medical research. Researchers at OpenAI have fine-tuned the LLaMA model for medical tasks and created the PMC-LLaMA model by training it on 4.8 million medical papers. The PMC-LLaMA model performs better on medical tasks than the original LLaMA model and includes more relevant medical knowledge. However, there are limitations to the current version of PMC-LLaMA, as it has only been trained for five epochs. In future work, the researchers plan to continuously train the model. The PMC-LLaMA model is more suitable for medical tasks than the foundation LLaMA model. Future work includes injecting more domain knowledge into pre-trained models and conducting a preliminary investigation by fine-tuning LLaMA. The researchers compared the outputs of PMC-LLaMA and the original LLaMA on several medical scenarios, including COPD, robotic cardiac surgery, and pneumonia, and found that PMC-LLaMA was more accurate and informative. The article discusses the improvements made to LLaMA, a language model for medical papers. The authors compare the performance of their modified model, PMC-LLaMA, to the original LLaMA and other language models like ChatGPT and InstructGPT. They show that PMC-LLaMA outperforms LLaMA and achieves competitive results even under zero-shot evaluation. The authors also demonstrate the effectiveness of data-efficient fine-tuning and full fine-tuning on different medical datasets. They conclude that PMC-LLaMA offers better initialization for medical tasks and converges faster than LLaMA. The PMC-LLaMA model can efficiently learn medical knowledge from downstream training data. The model is fine-tuned using the AdamW optimizer with a learning rate of 2e-5 and a batch size of 128 for 3 epochs. The model is evaluated on medical QA benchmarks, and the results are shown in Tab.1. The experiments are conducted on the USMLE dataset, and the data-efficient fine-tuning approach is used with the PEFT Low-Rank Adaptation (LoRA) method to reduce computation cost. The full fine-tuning approach and parameter-efficient fine-tuning approach are also evaluated. The datasets used for training and testing include USMLE, MedMCQA, and PubMedQA. The model achieves good results in all evaluation scenarios. This document outlines the fine-tuning procedure and benchmark descriptions for PMC-LLaMA, an open-source language model trained on 4.8 million biomedical academic papers. The model is trained for 5 epochs with 8 A100 GPUs in around 7 days using the Fully Sharded Data Parallel (FSDP) acceleration strategy and bf16 data format. During finetuning, the max context length is set as 512, with a batch size of 128 and the model is trained with AdamW optimizer. The dataset used is S2ORC Datasets with 81.1M English-language papers, filtered with PubMed Central (PMC)-id. The authors believe that a medical-specific foundational language model would be more suitable for specialization in various healthcare sub-tasks, such as medical dialogue or consultation. PMC-LLaMA has demonstrated superior performance on various medical QA datasets, including PubMedQA, MedMCQA, and USMLE. Further finetuning involves injecting domain knowledge into the pre-trained LLaMA to steer the foundational language model towards medical-specific corpus. Large language models (LLMs), such as GPT and GPT-4, have revolutionized artificial intelligence in various domains, including natural language processing, computer vision, and biomedical applications. However, these models often exhibit unsatisfactory performance in areas that value precision, such as medical applications, due to a lack of domain-specific knowledge. To address this issue, the authors introduce PMC-LLaMA, an open-source language model that is acquired by fine-tuning an existing LLM on a total of 4.8 million biomedical academic papers. The model demonstrates better understanding of biomedical domain-specific concepts and achieves high performance on biomedical QA benchmarks, including PubMedQA, MedMCQA, and USMLE, by injecting medical knowledge and enhancing its capability in the medical domain. Preliminary evaluations are conducted on three biomedical QA datasets. The authors are affiliated with Shanghai AI Laboratory and Cooperative Medianet Innovation Center at Shanghai Jiao Tong University.