如何评估你的微调模型：非技术指南
无需 ML 专业知识评估微调模型质量的实用框架——涵盖准确性检查、输出一致性、边缘案例测试和生产就绪度。
你微调了一个模型。训练完成没有错误。损失曲线下降了。然后呢？
"看起来合理"不是评估策略。以下是五种不需要 ML 专业知识的实用评估方法。
方法1：人工审查抽样
收集50-100个代表性输入，运行模型，让领域专家评估每个输出：正确、部分正确或错误。
方法2：A/B 对比基准
将同样的测试输入通过微调模型和基线模型，盲测对比。微调模型应至少赢得60%的对比。
方法3：金标准测试集
30-50个带已知正确输出的精选样本。永远不要用此数据训练。
方法4：边缘案例电池
30-50个边缘案例：模糊输入、超出范围输入、对抗性输入、边界条件。通过标准：零灾难性故障。
方法5：生产监控
跟踪输出长度分布、拒绝率、延迟、用户反馈信号。每周抽取20-30个随机生产输出进行人工审查。
常见评估错误
- 在训练数据上评估
- 只评估正常路径输入
- 使用单一指标
- 只评估一次就发布
- 因为信任训练数据而跳过评估
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