Zhiyuan Research Institute has released a new code generation training set TACO, aiming to improve the performance of code generation models. The TACO dataset is large in scale, high in quality, and provides diverse problem-solving answers and fine-grained labels, providing a more comprehensive benchmark for model training and evaluation. Its evaluation results show that there is a significant gap between the existing mainstream models and GPT-4. This not only highlights the role of TACO as a challenging test benchmark, but also points out the direction for the improvement of future code generation models, indicating that this field will continue to improve. There is huge potential for development.
Experimental results show that the currently popular code generation model is significantly different from GPT-4 in the TACO evaluation, indicating that there is still room for improvement in this field. The release of the TACO data set provides valuable resources for the improvement of code generation models and promotes the development of this field. It deserves the attention and in-depth study of researchers.
The emergence of TACO has brought new opportunities and challenges to the field of code generation. Its large-scale, high-quality data sets and detailed evaluation solutions will help promote the birth of more powerful and reliable code generation models. In the future, we can look forward to more research results based on TACO to further improve the level of code generation technology.