This week’s system design refresher: 8 Key Data Structures That Power Modern Databases How does ChatGPT work? Does the cloud really save costs? Amazon’s system architecture (1998 edition) New Machine Learning System Design Interview Book by ByteByteGo
I asked chatGPT if the explanation given here was accurate and comprehensive. It explained the corrections and then updated the article. <sigh>
Your explanation provides a good overview of the ChatGPT training process and how it answers a prompt, particularly the emphasis on safety checks and content moderation. However, I would like to suggest a few modifications and clarifications to make it more comprehensive:
Pre-training: Specify that the model learns to predict the next word in a sequence, given the context of previous words.
For the first step, mention that the training data can include demonstrations and comparisons.
For the second step, clarify that the reward model is trained using human preferences, where human evaluators rank different model-generated responses based on relevance and quality.
For the third step, specify that the model is fine-tuned using Proximal Policy Optimization (PPO).
Answer a prompt:
Consider renumbering the steps for better readability and understanding.
In Step 3-4, mention that the input is tokenized and then processed by the ChatGPT model to generate a response.
In Step 5-6, clarify that the generated response is detokenized before being sent to the content moderation component.
Here's the revised explanation:
Training ChatGPT involves two stages:
Pre-training: In this stage, a GPT model (decoder-only transformer) is trained on a large dataset consisting of text from the internet. The objective is to train a model that can predict the next word in a sequence, given the context of previous words, in a grammatically correct and semantically meaningful manner. After pre-training, the model can complete given sentences but is not yet capable of answering questions effectively.
Fine-tuning: This stage is a 3-step process that transforms the pre-trained model into a question-answering ChatGPT model:
a. Collect training data (questions and answers, demonstrations, and comparisons) and fine-tune the pre-trained model on this data. The model takes a question as input and learns to generate an answer similar to the training data.
b. Collect more data (questions with multiple answers) and train a reward model using human preferences. Human evaluators rank the answers based on relevance and quality.
c. Use reinforcement learning with Proximal Policy Optimization (PPO) to further fine-tune the model, making its answers more accurate and relevant.
Answering a prompt:
The user enters the full question, e.g., "Explain how a classification algorithm works."
The question is sent to a content moderation component, ensuring that it does not violate safety guidelines and filters out inappropriate questions.
If the input passes content moderation, it is tokenized and processed by the ChatGPT model. If the input doesn't pass content moderation, it goes straight to template response generation.
The ChatGPT model generates a response based on the input.
The generated response is detokenized before being sent to the content moderation component again. This ensures that the response is safe, harmless, unbiased, etc.
If the response passes content moderation, it is shown to the user. If the response doesn't pass content moderation, it goes to template response generation, and a template answer is shown to the user.