LlamaCare: A Large Medical Language Model for Enhancing Healthcare Knowledge Sharing
This work addresses the need for more precise and efficient medical AI tools for healthcare professionals, though it is incremental as it builds on existing LLM fine-tuning and classification methods.
The authors tackled the problem of general large language models (LLMs) giving imprecise answers in medical domains and redundant categorical outputs after instruction tuning, by proposing LlamaCare, a fine-tuned medical language model, and Extended Classification Integration (ECI) module. They achieved performance comparable to ChatGPT and state-of-the-art models with similar parameters, using a 24G GPU for lower carbon emissions.
Large language models (LLMs) have shown amazing capabilities in knowledge memorization and the present. However, when it comes to domain-specific knowledge and downstream tasks like medical, general LLMs are often unable to give precise answers. In addition, when people want LLMs to answer classification questions, they usually go through instruction tuning first. However, LLMs do not always give a direct index of the categorization after instruction tuning. In this paper, we proposed LlamaCare, a fine-tuned medical language model, and Extended Classification Integration(ECI), a module to handle classification problems of LLMs. Our contributions are : (i) We fine-tuned a large language model of medical knowledge with very low carbon emissions and achieved similar performance with ChatGPT by a 24G GPU. (ii) We solved the problem of redundant categorical answers and improved the performance of LLMs by proposing a new module called Extended Classification Integration. (iii) We released our processed data for one-shot and few-shot training for some benchmarks such as PubMedQA and USMLE 1-3 step. Our method achieves a close performance comparable to some state-of-the-art models with the same quantity of parameters on benchmarks, while being more environmentally friendly by using less GPU computation time. Our models, codes, and datasets can be found at \url{https://github.com/Stephen-SMJ/LLamaCare}.