When Helpful Examples Still Lead to Refusal
TL;DR for operators Correctly labeled helpful examples do not necessarily eliminate false refusals in safety-aligned language models. Training updates from harmful requests can interfere with learning to answer legitimate requests that contain sensitive language. Chen and colleagues introduce GOAL, a fine-tuning method that selectively removes conflicting gradient components from helpful-response training and separately encourages the model to distinguish harmful from harmless-but-sensitive prompts.1 Unlike simply reducing safety supervision, the method leaves harmful-example training gradients intact. ...