Decoding the Training Methodology of ChatGPT: A Comprehensive Look into Fine-Tuning GPT-3 for NLP
ChatGPT is a state-of-the-art language processing model developed by OpenAI. It is a variant of GPT-3, which is a pre-trained model trained on a broad distribution of internet data. ChatGPT is fine-tuned using a combination of supervised and reinforcement learning, making it capable of performing a variety of natural language processing tasks such as dialogue systems, question answering, and text generation. In this article, we will delve into the training process of ChatGPT and understand how it was fine-tuned to achieve its high level of performance.
The training process of ChatGPT began with a pre-trained GPT-3 model. The team at OpenAI then collected typical human prompts used for GPT from the OpenAI website and asked labelers and customers to write down the correct output. This resulted in 12,725 labeled data which was used to fine-tune the pre-trained GPT-3 model.
Once the supervised fine-tuning was completed, the team at OpenAI sampled more human prompts and generated multiple outputs from the model. A labeler was then asked to rank those outputs. The resulting data was used to train a Reward model, which was fine-tuned using 33,207 prompts and around 10 times more training samples using different combinations of the ranked outputs.