Understanding the Training of Large Language Models (LLMs)

Hello, everyone!

Before diving into the exciting world of generative AI applications using large language models (LLMs) or multimodal models, it’s crucial to understand how these models are specifically trained. While practical implementation from scratch isn’t feasible due to the enormous resources required, I will walk you through the theoretical steps of training LLMs using some well-known models like OpenAI’s ChatGPT and Meta’s LLaMA.

Training LLMs: A Step-by-Step Guide

Training a large language model (LLM) from scratch involves a detailed and structured process. Let’s break down these steps:

Stage 1: Generative Pre-Training

1. Data Collection: The initial step involves gathering vast amounts of text data from various sources, such as websites, articles, books, and public forums.
2. Transformer Architecture: The core of training involves transformers, which consist of encoder-decoder architectures. Transformers are adept at tasks like language translation, text summarization, text completion, and sentiment analysis.
3. Training the Base Model: The collected data is fed into the transformer model to create a base GPT (Generative Pre-trained Transformer) model. This model is capable of performing the aforementioned tasks but requires further tuning for specific applications.

Stage 2: Supervised Fine-Tuning

1. Creating a Training Corpus: This stage involves human agents simulating conversations to generate request-response pairs. These interactions form a supervised fine-tuning (SFT) training dataset.
2. Training with Supervised Data: The base GPT model is fine-tuned using the SFT corpus, optimizing the model using algorithms like stochastic gradient descent to produce an SFT ChatGPT model.

Stage 3: Reinforcement Learning with Human Feedback (RLHF)

1. Generating Responses: The SFT ChatGPT model generates multiple responses to given prompts.
2. Human Ranking: Human evaluators rank these responses based on their suitability and correctness.
3. Reward Model Creation: A reward model is developed using the rankings to score responses, aiding in the classification of good and bad outputs.
4. Reinforcement Learning: The reward model is optimized using techniques like Proximal Policy Optimization (PPO), continuously improving the ChatGPT model based on human feedback.

Practical Example of LLM Training

Imagine a chef who can cook various dishes. A customer requests a non-vegetarian dish for dinner. The chef gathers opinions from multiple people to decide on a dish. Based on the feedback, the chef ranks the dishes and creates a reward system to determine the best choice. This analogy illustrates how responses are ranked and used to train the model further.

Importance of Transformers

Transformers are integral to LLMs, providing the foundation for models like ChatGPT and BERT. They use mechanisms like “attention is all you need,” enabling efficient processing of vast amounts of data. Understanding transformers is crucial for grasping the functionality of advanced AI models.

Final Thoughts

This video aims to provide a theoretical understanding of how LLMs like ChatGPT are trained. While practical implementation requires extensive resources, the concepts discussed here form the foundation of generative AI applications. Make sure to watch the entire session for a comprehensive understanding, and don’t forget to check out the recommended article by Pradeep Menon for additional insights.

Thank you, and enjoy the session!