Real World Code
Real-world code research focuses on bridging the gap between large language models (LLMs) and practical software development, aiming to improve the quality, security, and efficiency of automatically generated code. Current research emphasizes developing methods for generating equivalent code representations, ensuring code correctness through techniques like hierarchical debugging and polyhedral modeling, and mitigating security vulnerabilities via prompt optimization and generative adversarial networks. This field is significant because it directly impacts software engineering practices, potentially increasing developer productivity and improving software reliability and security.
Papers
Model Cascading for Code: Reducing Inference Costs with Model Cascading for LLM Based Code Generation
Boyuan Chen, Mingzhi Zhu, Brendan Dolan-Gavitt, Muhammad Shafique, Siddharth Garg
GECKO: Generative Language Model for English, Code and Korean
Sungwoo Oh, Donggyu Kim
OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code
Maxence Faldor, Jenny Zhang, Antoine Cully, Jeff Clune
ChatGPT Code Detection: Techniques for Uncovering the Source of Code
Marc Oedingen, Raphael C. Engelhardt, Robin Denz, Maximilian Hammer, Wolfgang Konen