Chapter 09.01: Instruction Fine-Tuning
Instruction fine-tuning aims to enhance the adaptability of large language models (LLMs) by providing explicit instructions or task descriptions, enabling more precise control over model behavior and adaptation to diverse contexts. This approach involves fine-tuning LLMs on task-specific instructions or prompts, guiding the model to generate outputs that align with the given instructions. By conditioning the model on explicit instructions, instruction fine-tuning facilitates more accurate and tailored responses, making LLMs more versatile and effective in various applications such as language translation, text summarization, and question answering.