chatgpt api
parameters
two important parameters that you can use with OpenAI’s GPT API to help control text generation behavior: temperature and top_p sampling.
Temperature is a parameter that controls the “creativity” or randomness of the text generated by GPT-3. A higher temperature (e.g., 0.7) results in more diverse and creative output, while a lower temperature (e.g., 0.2) makes the output more deterministic and focused.In practice, temperature affects the probability distribution over the possible tokens at each step of the generation process. A temperature of 0 would make the model completely deterministic, always choosing the most likely token.
Top_p sampling is an alternative to temperature sampling. Instead of considering all possible tokens, GPT-3 considers only a subset of tokens (the nucleus) whose cumulative probability mass adds up to a certain threshold (top_p).
chatgpt methods
We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as InstructGPT, but with slight differences in the data collection setup. We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides—the user and an AI assistant. We gave the trainers access to model-written suggestions to help them compose their responses. We mixed this new dialogue dataset with the InstructGPT dataset, which we transformed into a dialogue format.
To create a reward model for reinforcement learning, we needed to collect comparison data, which consisted of two or more model responses ranked by quality. Using these reward models, we can fine-tune the model using Proximal Policy Optimization. We performed several iterations of this process.
