Large language models (LLMs) have shown amazing capabilities in knowledge
memorization and 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 effect with the
state-of-the-art model in benchmarks while costing lower GPU resources compared
to LLMs with the same quantity of parameters. Our models, codes, and datasets
can be found in this https URL

提出了使用低碳排放量进行细调的医疗语言模型 LlamaCare，以及处理 LLMs 分类问题的新模块 Extended Classification Integration (ECI)。通过在 PubMedQA 和 USMLE 等基准测试中进行一次性和少次性训练，我们的方法在成本相对较低的情况下实现了与最新模型接近的效果。