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thinksoso avatar thinksoso commented on July 19, 2024

你的见解是合理的，手动构建模板无法保证指令多样性，我们通过self-struct的方法构建指令数据集，由于gpt3.5功能比gpt3更强大，所以我们使用gpt3.5一次性生成多个提问和回答。保证了多样性之后，我们对一些重要的疾病手动设计了生成模板进行增强生成，并吸收了现有的问答数据。具体的知识库和生成代码还在整理中，感谢关注！

from med-chatglm.

RealDuxy avatar RealDuxy commented on July 19, 2024

感谢回复。所以我总结一下，你们的做法是
方法1. 利用gpt3.5阅读某条疾病医疗知识，然后针对这一条疾病医疗知识生成多个提问和回答。
方法2. 部分重要疾病则是利用方法1 + 手动设计模版

最后的数据集合是方法1+方法2+现有的问答数据。

关于方法1，我自己通过修改belle项目中的prompt，尝试过让chatgpt(gpt3.5-turbo)先阅读材料，然后针对该材料提问和回答，发现效果并不理想（格式和内容质量都很不稳定）。对于医疗问答任务来说对事实性要求相对来说会更高（所以希望他根据给定医疗知识文本提问和回答而不是凭空生成），像请问你们是如何处理这样的问题呢？单纯的调整prompt似乎并不能满足对于指令数据质量的要求

from med-chatglm.

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