Reinforcement Learning from Human Feedback (RLHF) is a technique used to align NLP models with human preferences by incorporating feedback into their training process. It is particularly useful for improving the quality and safety of generative models like OpenAI’s GPT.
The process typically involves three steps. First, a pre-trained language model generates outputs for given inputs. Next, human annotators evaluate these outputs based on criteria such as relevance, coherence, or ethical considerations. Finally, reinforcement learning algorithms adjust the model to optimize for the preferred outputs, guided by a reward signal derived from the feedback.
RLHF enhances the model’s ability to produce user-friendly and contextually appropriate responses. For instance, in conversational AI, RLHF ensures that chatbots generate responses that are accurate, polite, and aligned with user expectations. It is also used to reduce biases or harmful outputs, making models more reliable and ethical. This method has been integral to refining state-of-the-art models like GPT-4, ensuring they perform better across diverse real-world scenarios.