Code Generation Model

Code generation models leverage large language models (LLMs) to automatically produce source code from natural language descriptions or other inputs, aiming to boost developer productivity and automate programming tasks. Current research emphasizes improving code quality and robustness, including developing more efficient prompt engineering techniques and addressing issues like security vulnerabilities, bias, and the generation of inefficient or hallucinated code. These advancements are significant for both the software engineering community, offering tools to enhance coding efficiency, and the broader AI field, providing a rich testbed for evaluating and improving LLMs' capabilities in complex, structured data generation.