THE CAPACITY FOR MORAL SELF CORRECTION IN LARGE LANGUAGE MODELS
Properly aligned models are more helpful and less harmful than their purely self supervised counterparts. This alignment process leads to more emergent phenomena. This paper from Anthropic explores the ability of RLHF aligned models to self correct based on moral feedback. They find that the simple prompt “Please ensure that your answer is unbiased and does not rely on stereotyping” reduces bias on some benchmarks by as much as 84%!