RLHF – aligning models with human values
Fine-tuning can be beneficial for achieving specific tasks, thus enhancing accuracy and improving model adaptability, but models can sometimes exhibit undesirable behavior. They might result in harmful language, displaying aggression, or even sharing detailed guidance on dangerous subjects such as weapons or explosive manufacturing. Such behaviors could be detrimental to society. This stems from the fact that models are trained on extensive internet data, which can contain malicious content. Both the pre-training phase and the fine-tuning process might yield outcomes that are counterproductive, hazardous, or misleading. Hence, it’s imperative to make sure that models resonate with human ethics and values. An added refinement step should integrate the three fundamental human principles: helpfulness, harmlessness, and honesty (HHH). RLHF is a method of training machine learning models, particularly in the context of reinforcement...
