Can RLHF improve model safety?

RLHF incorporates human preferences into training, steering models away from generating harmful, biased, untruthful, or unsafe responses. Its effectiveness depends crucially on the quality, diversity, and comprehensiveness of the human feedback data. It primarily targets alignment-related safety concerns in output generation. However, it isn't a panacea and cannot guarantee perfect safety; biases in the feedback data or unforeseen scenarios may still lead to failures.

RLHF improves safety by refining model outputs using human-defined priorities. It involves collecting human comparisons of model responses, training a reward model to reflect these preferences, and subsequently fine-tuning the language model via reinforcement learning. This process reduces harmful outputs like toxic language or misinformation, and promotes desirable behaviors like truthfulness and helpfulness. Consequently, RLHF is a key technique used by major AI developers to make models safer for public deployment.