How ChatGPT is Trained: A Peek Behind the AI Curtain 🧠✨
Artificial intelligence has transformed the way we interact with technology, and one of the most fascinating advancements is ChatGPT. Developed by OpenAI, ChatGPT is a sophisticated language model that can generate human-like text based on the input it receives. But how exactly is ChatGPT trained to achieve such impressive capabilities? This article delves into the training process of ChatGPT, highlighting the key steps involved.
Step 1: Collect Demonstration Data and Train a Supervised Policy
The first step in training ChatGPT involves collecting demonstration data and training a supervised policy. Here’s how it works:
Prompt Sampling:
A prompt is sampled from a vast dataset of prompts. For instance, “Explain the moon landing to a 6-year-old.”
Labeler Demonstration:
A labeler, typically a human expert, demonstrates the desired output behavior. For example, they might explain, “Some people went to the moon…”
Supervised Fine-Tuning (SFT):
This demonstration data is used to fine-tune the GPT-3 model with supervised learning. The labeler’s response helps the model understand how to generate coherent and contextually relevant responses to similar prompts in the future.
Step 2: Collect Comparison Data and Train a Reward Model
The second step focuses on collecting comparison data and training a reward model to improve the quality of the generated outputs:
Model Output Sampling:
A prompt and several model outputs are sampled. For example, multiple explanations for the moon landing are generated.
Ranking by Labelers:
A labeler ranks these outputs from best to worst based on clarity, relevance, and accuracy.
Training the Reward Model (RM):
This ranking data is used to train the reward model. The reward model learns to predict which outputs are better based on the labeler’s rankings. This step is crucial for refining the model’s ability to generate high-quality responses.
