To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation

1. 1Identify the limitations of existing approaches to private-library-oriented code generation
2. 2Develop new methods for teaching LLMs to use private libraries, such as incorporating library-specific knowledge into the model's training data
3. 3Evaluate the effectiveness of these new methods in generating code that utilizes private libraries correctly
4. 4Refine the models and methods based on the evaluation results to improve performance

AI engineers and researchers working on code generation tasks can benefit from this study, as it highlights the limitations of current approaches and proposes new methods for improving private-library-oriented code generation

💡 Providing API documentation alone is insufficient for teaching LLMs to use private libraries for code generation

🤖 LLMs struggle with private-library-oriented code generation. New study proposes novel approaches to improve performance 🚀