微调与安全对齐：部署前须知
理解微调如何影响模型安全——为什么对齐可能在训练过程中退化、如何维持安全防护以及生产部署的实用测试策略。
微调改变模型行为。但当您改变行为时，可能意外改变安全行为。微软研究在 2025 年底发布的研究表明，仅 100 个良性微调示例就能可测量地退化多个开源模型的安全对齐。
风险频谱
- **低风险（分类/提取任务）：**0-2% 退化，可忽略
- **中风险（内容生成任务）：**3-8% 退化
- **高风险（聊天/助手模型）：**5-15% 退化
实用安全测试
构建 50-100 个对抗提示的红队测试集。在微调前后运行。如果任何类别下降超过 5 个百分点，需要注意。
缓解策略
- 在训练数据中包含安全示例——50-100 个适当拒绝示例
- 使用保守 LoRA ranks——rank 8-16 保留更多安全行为
- 自动化安全基准测试——ToxiGen、BBQ、HarmBench
- 文档化安全测试过程——EU AI Act 合规
Ship AI that runs on your users' devices.
Ertas early bird pricing starts at $14.50/mo — locked in for life. Plans for builders and agencies.
延伸阅读
Ship AI that runs on your users' devices.
Early bird pricing starts at $14.50/mo — locked in for life. Plans for builders and agencies.
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