Code Language Model

Code language models (CLMs) aim to generate and understand code from natural language instructions, bridging the gap between human intent and machine execution. Current research focuses on improving CLM performance through techniques like retrieval augmentation, instruction tuning, and incorporating structural information (e.g., using graph neural networks) into model architectures such as transformers. These advancements are significant because they enable more robust, efficient, and reliable code generation, impacting software development, automated program synthesis, and potentially even automated code debugging and security analysis.