LLM Fine-Tuning, Continuous Pre-Training, and Reinforcement Learning through Human Feedback (RLHF): A Comprehensive Guide
Introduction Large Language Models (LLMs) are artificial neural networks designed to process and generate human-like language. They're trained on vast amounts of text data to learn patterns, relationships, and context. In this article, we'll explore three essential techniques for refining LLMs: fine-tuning, continuous pre-training, and Reinforcement Learning through Human Feedback (RLHF). 1. LLM Fine-Tuning Fine-tuning involves adjusting a pre-trained LLM's weights to adapt to a specific task or dataset. Nature: Supervised learning, task-specific adaptation Goal: Improve performance on a specific task or dataset Example: Fine-tuning BERT for sentiment analysis on movie reviews. Example Use Case: Pre-trained BERT model Dataset: labeled movie reviews (positive/negative) Fine-tuning: update BERT's weights to better predict sentiment 2. Continuous Pre-Training Continuous pre-training extends the initial pre-training phase of an LLM. It involves adding new data to the pre-...