How Large Language Models (LLM) In Generative AI Are Trained ?

Introduction

Welcome to my YouTube channel! My name is Krishnaik, and I have been uploading videos related to data science for over three years. In my videos, I cover various topics in data science, including machine learning, deep learning, neural networks, NLP, and end-to-end projects with deployments. Today, we will delve into the training process of large language models (LLM) in generative AI, specifically focusing on how chat GPT is trained.

Introduction to Large Language Models (LLM)

Large language models are massive models trained on a vast amount of data to solve specific problem statements. These models can be text-to-text models, such as chatbots for conversations, or text-to-image, text-to-video, or text-to-audio models. For example, Chat GPT 3.5, which has been trained with 175 billion parameters, is a type of LLM.

Training Stages of Chat GPT

Training a Chat GPT model involves three stages: generative pre-training, supervised fine-tuning, and reinforcement learning with human feedback.

Stage 1: Generative Pre-training

In this stage, a large dataset of internet text data, including website articles, books, public forums, and tutorials, is used as input. The data is passed through Transformers, which use the encoded-decoder architecture. The Transformers model creates a base GPT model capable of performing various language-related tasks, such as language translation, text summarization, and sentiment analysis.

Stage 2: Supervised Fine-tuning

In supervised fine-tuning, real conversations between two humans are recorded and converted into a training data corpus consisting of requests and responses. Multiple conversations and alternative responses are captured to create a diverse dataset. This dataset is then used to train the base GPT model using stochastic gradient descent. The outcome is an SFT (Supervised Fine-tuning) Chat GPT model.

Stage 3: Reinforcement Learning with Human Feedback

To improve the accuracy and response quality of the Chat GPT model, reinforcement learning with human feedback is applied. Real conversations between humans occur, and the alternative responses generated by the model are ranked based on suitability. This ranking is used to create a reward model, which assigns scores to each response based on probability. The model is updated using Proximal Policy Optimization (PPO) to improve response selection. This process ensures that the Chat GPT model responds appropriately based on human feedback.

Summary

The process of training large language models in generative AI, such as Chat GPT, involves three stages: generative pre-training, supervised fine-tuning, and reinforcement learning with human feedback. The model is first pre-trained on a vast amount of internet text data using Transformers. This pre-training allows the model to perform language-related tasks. Real conversations are then used to fine-tune the model and generate a more accurate response. Finally, reinforcement learning with human feedback is applied to further improve the model's response quality.

Keywords

Large language models, Chat GPT, Generative AI, Training stages, Generative pre-training, Supervised fine-tuning, Reinforcement learning, Human feedback, Transformers, Proximal Policy Optimization (PPO).

FAQ

Q: What is the purpose of generative pre-training in training large language models? A: Generative pre-training involves training large language models on a vast amount of internet text data to create a base model capable of performing language-related tasks.

Q: How is supervised fine-tuning used in training Chat GPT models? A: Supervised fine-tuning involves using real conversations between humans to create a training data corpus consisting of requests and responses. This data is then used to train the base GPT model, resulting in a more accurate response generation.

Q: What is reinforcement learning with human feedback in training large language models? A: Reinforcement learning with human feedback is a technique used to improve the response quality of large language models. Real conversations between humans are used to rank alternative responses, and a reward model is created based on this ranking. The model is then updated using Proximal Policy Optimization (PPO) to enhance response selection.

Q: How does the training process of large language models in generative AI impact response accuracy? A: The training process helps improve the accuracy of large language models by incorporating massive amounts of data, fine-tuning with real conversations, and applying reinforcement learning with human feedback. These steps enhance the model's ability to generate appropriate responses based on the given input.

Q: What are the potential applications of large language models in generative AI? A: Large language models have various applications, including chatbots, language translation, text summarization, sentiment analysis, and image generation. They can be used in AI-powered customer support systems, content generation, and data analysis, among others.