Instruction Following Model

Instruction-following models aim to create large language models (LLMs) that reliably and accurately complete tasks specified by user instructions. Current research emphasizes improving these models through techniques like optimizing prompts (both instructions and exemplars), efficiently training models on diverse datasets (e.g., using distribution editing), and developing robust evaluation metrics that go beyond simple accuracy. This work is significant because it addresses the critical need for reliable and controllable LLMs, impacting various fields from scientific literature analysis to graphic design and code generation.