为开放权重模型微调构建边缘案例数据集，最难的部分是什么？

What's the hardest part of creating edge cases for fine-tuning open-weight models? [D]

AI 导读

有开发者提问：为开放权重模型微调构建边缘案例数据集时，最难的是哪一环。问题覆盖如何识别罕见边缘案例、在已有数据集上构造边缘案例、自写还是用模型生成合成数据或挖掘真实数据、如何筛掉错误重复和不真实的样本、如何判断失败案例覆盖是否足够，以及增加边缘案例后模型反而变差或没有提升的经历。

正文

I'm interested in how people build datasets for fine-tuning open-weight models, especially when it is about making a model handling edge cases than just perform well on common examples.

I'm curious about the data side of the process.

Finding edge cases: How do you identify edge cases that are rare or unusual. Also how do you make edge cases on an existing dataset to make it more specialized for what you need.
Generating examples: Do you write them by yourself, or use a good model to generate synthetic data, mine real data, etc?
Quality control: How do you catch incorrect, repetitive, or unrealistic examples?
Coverage: How do you know when your dataset covers enough of the failure cases that matter?
Measuring impact: Have you had cases where adding more edge cases actually made the model worse or failed to improve it?

submitted by /u/Humble-Shopping-9495
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