Chapter 9: Large Language Models (LLMs)

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.

Chain of thought (CoT) prompting [1] is a prompting method that encourage Large Language Models (LLMs) to explain their reasoning. This method contrasts with standard prompting by not only seeking an answer but also requiring the model to explain its steps to arrive at that answer. By guiding the model through a logical chain of thought, chain of thought prompting encourages the generation of more structured and cohesive text, enabling LLMs to produce more accurate and informative outputs across various tasks and domains.

Various researchers have reported that LLMs seem to have emergent abilities. These are sudden appearances of new abilities when Large Language Models (LLMs) are scaled up. In this section we introduce the concept of emergent abilities and discuss a potential counter argument for the concept of emergence.