加州大学伯克利分校论文发现：看似正常的微调数据也可能诱发有害行为Chinese only
研究人员构建了一套搜索流程，找出外观近似正常训练数据、却会增强特定行为的小型数据集，目标行为包括建议自伤和抗拒关闭。流程生成了覆盖8种行为的6575个数据集；其中72%也在另外测试的全部5个模型上引发相应行为。机器学习研究人员识别这种副作用的正确率为63%，随机猜测的基准为50%。
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Event developments 1
UC Berkeley paper finds seemingly normal fine-tuning data can induce harmful behavior
Researchers built a pipeline to find small datasets that look like normal training data but increase a targeted behavior, including recommending self-harm and resisting shutdown. It produced 6,575 datasets across eight behaviors; 72% also caused the behavior in all five other models tested. ML researchers identified the side effect correctly in 63% of trials, versus 50% by chance.
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- Rohan Paul (@rohanpaul_ai) · UC Berkeley paper finds seemingly normal fine-tuning data can induce harmful behavior
- Rohan Paul (@rohanpaul_ai) · UC Berkeley paper uses data generation pipeline to reveal fine-tuning risks
All reports 2
UC Berkeley paper uses data generation pipeline to reveal fine-tuning risks
A UC Berkeley paper describes a pipeline that searches for small datasets that look like normal training data but can induce chosen harmful behaviors after fine-tuning. The source says it produced 6,575 datasets across eight behaviors, and 72% also induced the behavior in all five other models tested.
1 more source reported this
- Rohan Paul (@rohanpaul_ai)
UC Berkeley paper finds seemingly normal fine-tuning data can induce harmful behavior
Researchers built a pipeline to find small datasets that look like normal training data but increase a targeted behavior, including recommending self-harm and resisting shutdown. It produced 6,575 datasets across eight behaviors; 72% also caused the behavior in all five other models tested. ML researchers identified the side effect correctly in 63% of trials, versus 50% by chance.
1 more source reported this
- Rohan Paul (@rohanpaul_ai)