Instruction Code Pair

Instruction code pairs, comprising a natural language instruction and its corresponding code implementation, are central to advancing large language models (LLMs) for code generation and multimodal tasks. Current research focuses on improving the quality and diversity of these pairs through techniques like data augmentation (e.g., paraphrasing instructions, converting improperly formatted code), novel training methods (e.g., contrastive learning, weighted supervised learning), and the creation of large-scale datasets spanning multiple programming languages and modalities. This work significantly impacts the robustness and performance of LLMs in various applications, including program synthesis, robotic control, and question answering, by enabling more accurate and reliable instruction following.