Instruction Tuned Large Language Model

Instruction-tuned large language models (LLMs) are designed to improve the ability of LLMs to follow instructions accurately and generate relevant responses, addressing limitations of previous models. Current research focuses on improving instruction-following capabilities through techniques like continual pretraining, model merging, and reinforcement learning from human feedback, often applied to architectures such as Llama and GPT models. This area is significant because it enhances the reliability and safety of LLMs for various applications, including finance, healthcare, and software development, while also raising important questions about bias mitigation and robustness to adversarial attacks.