zyx1234/MuSeR_GPT_OSS_120B_Distillation

该数据集包含约10万条合成的医学查询和相应的回答，这些数据是从GPT-OSS-120B中提取的。生成这些合成医学查询的方法遵循了一篇名为《通过多方面自我细化学习增强LLMs的医学上下文感知能力》的论文中提出的属性条件生成方法。通过在这个数据集上进行监督微调，可以显著提高大型语言模型（LLMs）的医学对话能力。This dataset contains ~100k synthetic medical queries and corresponding responses distilled from GPT-OSS-120B. The generation of synthetic medical queries follows an attribute-conditioned generation method proposed in the paper *Enhancing the Medical Context-Awareness Ability of LLMs via Multifaceted Self-Refinement Learning*. We found that supervised fine-tuning on this dataset can substantially improve LLMs medical conversational capabilities.