Research that enhances large language models to generate higher-quality code that meets professional standards and best practices, not just functional requirements.

• Addresses common LLM code generation issues like poor style and maintainability
• Uses innovative Comparative Prefix-Tuning technique to improve output quality
• Reduces developer effort needed to clean up AI-generated code
• Preserves the efficiency benefits of using LLMs in development workflows

For engineering teams, this research represents a significant step toward making AI code assistants that produce professional-grade code requiring minimal human refinement—potentially increasing developer productivity while maintaining high standards.

Enhancing High-Quality Code Generation in Large Language Models with Comparative Prefix-Tuning