The latest research shows that Microsoft's WaveCoder model has achieved significant breakthroughs in code generation tasks. The research team effectively generated high-quality and diverse instruction data for model training through extensive instruction fine-tuning and using the CodeOcean data set to build an LLM-based generator-discriminator framework. This study details the complete process from original code to final model training, providing new ideas and methods for improving the performance of code-based large language models.
The latest research points out that the Microsoft WaveCoder model performs well on different coding tasks through extensive instruction tuning. The study introduces the CodeOcean data set and proposes a generator-discriminator framework based on LLM to generate diverse high-quality instruction data. The WaveCoder model outperforms other models in various tasks, verifying its efficiency. The study details the entire process from original code to trained model and highlights the important contribution of the proposed method in improving code LLM performance.
The success of the WaveCoder model proves the effectiveness of the LLM-based generator-discriminator framework and extensive instruction tuning strategies in improving the performance of large-scale language models in code. This research provides valuable insights for future code generation model improvements and also heralds the further development and application of code generation technology.