Instruction Tuned Model

Instruction tuning refines large language models (LLMs) by fine-tuning them on datasets of instructions and desired responses, aiming to improve their ability to follow diverse instructions and generate more helpful and accurate outputs. Current research focuses on developing efficient instruction datasets (including programmatic generation), exploring various model architectures and parameter-efficient fine-tuning techniques like LoRA, and evaluating model performance across diverse tasks and benchmarks, including those assessing reasoning, code generation, and multilingual capabilities. This field is significant because it enhances the practical usability of LLMs, enabling their deployment in a wider range of applications while also providing valuable insights into model behavior and alignment with human intentions.