IvyGPT：医疗领域交互式中文路径语言模型
IvyGPT: InteractiVe Chinese pathwaY language model in medical domain
AI总结：
针对通用大语言模型医疗应用准确率低的问题，提出基于LLaMA的医疗中文模型IvyGPT，结合医疗QA数据、RLHF和QLoRA训练330亿参数，性能超越同类医疗GPT模型。
摘要：
ChatGPT等通用大语言模型（LLM）已取得显著成效，但这类模型因准确率低、无法提供医疗建议，尚未在医疗场景广泛应用。我们提出IvyGPT，这是一款基于LLaMA的大语言模型，采用高质量医疗问答（QA）样本与人类反馈强化学习（RLHF）进行训练和微调。经过监督微调后，IvyGPT具备良好的多轮对话能力，但在综合诊断等其他方面无法达到医生水平。通过RLHF，IvyGPT可输出更丰富、更贴近人类的诊疗答案。训练过程中，我们使用QLoRA技术，在少量NVIDIA A100（80GB）GPU上训练了330亿参数。实验结果表明，IvyGPT的表现优于其他医疗GPT模型。
英文摘要：
General large language models (LLMs) such as ChatGPT have shown remarkable success. However, such LLMs have not been widely adopted for medical purposes, due to poor accuracy and inability to provide medical advice. We propose IvyGPT, an LLM based on LLaMA that is trained and fine-tuned with high-quality medical question-answer (QA) instances and Reinforcement Learning from Human Feedback (RLHF). After supervised fine-tuning, IvyGPT has good multi-turn conversation capabilities, but it cannot perform like a doctor in other aspects, such as comprehensive diagnosis. Through RLHF, IvyGPT can output richer diagnosis and treatment answers that are closer to human. In the training, we used QLoRA to train 33 billion parameters on a small number of NVIDIA A100 (80GB) GPUs. Experimental results show that IvyGPT has outperformed other medical GPT models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
