Task Instruction

Task instruction research focuses on improving large language models' (LLMs) ability to understand and execute instructions given in natural language, aiming to bridge the gap between human-understandable commands and machine-executable tasks. Current research emphasizes developing effective instruction embeddings, optimizing prompt design (including in-context examples and multi-stage prompting), and leveraging techniques like parameter-efficient fine-tuning (e.g., LoRA) and mixture-of-experts models to enhance LLMs' performance on diverse tasks. This field is crucial for advancing human-computer interaction and enabling LLMs to solve real-world problems more effectively, particularly in scenarios with limited labeled data or complex, multi-step instructions.