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ChatGPT Training: Understanding the Process of Fine-Tuning GPT-3 for Language Processing Tasks
ChatGPT is a variant of GPT-3, which was fine-tuned using a combination of supervised and reinforcement learning. The process involved collecting labeled data from human labelers, fine-tuning the model with this data, and then further fine-tuning it with a Reward model and the Proximal Policy Optimization (PPO) algorithm. The final model used a relatively small amount of data, specifically 12,725 labeled data for supervised fine-tuning, 33,207 prompts for the Reward model and 31,144 prompts for fine-tuning with the PPO algorithm. The process is detailed in the paper "InstructGPT: Generating Long-Form Text via Multi-Stage Retrieval-Generation" which can be found at https://arxiv.org/pdf/2203.02155.pdf.
How is this relevant?
This information is relevant because it provides insight into the training process of ChatGPT, a popular language model used for a variety of natural language processing tasks such as dialogue systems, question answering, and text generation. Understanding how the model was trained can help in understanding its capabilities and limitations, and can also be useful for determining the appropriate use-cases for the model. Additionally, the process of fine-tuning GPT-3 models with a relatively small amount of data is a significant achievement, as it allows for creating high-performing models with less data than would typically be required.
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