Chat models are capable of answering a wide range of questions, however, the accuracy of their responses is highly uncertain. In this research, we propose a specialized PEFT-MedAware model where we utilize parameter-efficient fine-tuning (PEFT) to enhance the Falcon-1b large language model on specialized MedQuAD data consisting of 16,407 medical QA pairs, leveraging only 0.44% of its trainable parameters to enhance computational efficiency. The paper adopts data preprocessing and PEFT to optimize model performance, complemented by a BitsAndBytesConfig for efficient transformer training. The resulting model was capable of outperforming other LLMs in medical question-answering tasks in specific domains with greater accuracy utilizing limited computational resources making it suitable for deployment in resource-constrained environments. We propose further improvements through expanded datasets, larger models, and feedback mechanisms for sustained medical relevancy. Our work highlights the efficiency gains and specialized capabilities of PEFT in medical AI, outpacing standard models in precision without extensive resource demands. The proposed model and data are released for research purposes only.

翻译：聊天模型能够回答广泛的问题，但其响应的准确性高度不确定。在本研究中，我们提出了一种专门的PEFT-MedAware模型，利用参数高效微调（PEFT）增强Falcon-1b大型语言模型，在包含16,407个医学问答对的专门MedQuAD数据集上，仅使用0.44%的可训练参数来提升计算效率。本文采用数据预处理和PEFT优化模型性能，并辅以BitsAndBytesConfig实现高效的Transformer训练。最终模型在特定领域的医学问答任务中，能够以更高准确性超越其他大型语言模型，同时利用有限计算资源，使其适用于资源受限环境下的部署。我们提出进一步改进方案，包括扩展数据集、使用更大模型以及引入反馈机制以维持医学相关性。本研究突显了PEFT在医学人工智能领域的效率提升和专业化能力，在无需大量资源需求的情况下，在精度上超越标准模型。所提出的模型和数据仅用于研究目的公开发布。