Human Feedback Reinforcement Learning
Traditionally, RL algorithms rely on predefined reward functions that provide feedback to the model based on its actions. However, designing an optimal reward function can be challenging, as it requires domain expertise and may not always capture the nuances of complex tasks, especially in case of chatbots. This is where human feedback comes into play.
HFRL leverages human feedback to train RL models more effectively. The idea is to incorporate human preferences and judgments into the learning process, allowing the model to learn from human demonstrations or evaluations. This feedback can be in the form of explicit rewards, rankings, or comparisons, depending on the task and the type of feedback collected.
In context of HFRL in chatbots, it is used to improve the quality of the chatbot’s responses over time. The chatbot receives feedback from human users in the form of ratings or explicit corrections, which it then uses to update its response generation algorithm.
Suppose a user asks the chatbot a question about a specific topic, and the chatbot generates a response. The user may rate the response on a scale of 1 to 5, with 5 being the best possible rating. If the user rates the response as a 3, the chatbot receives feedback that the response needs improvement or the chatbot can also use negative feedback to identify and avoid generating low-quality responses in the future.
It’s important to note that HFRL in chatbots like ChatGPT also involves balancing the exploration-exploitation trade-off. The chatbot must explore different response generation strategies to discover new, potentially better responses, while also exploiting its current knowledge to generate responses that are likely to be helpful. The feedback from users helps guide the chatbot’s exploration-exploitation trade-off, allowing it to find better responses while avoiding generating low-quality responses.
