Evaluating large language models trained on code
OpenAI published a study evaluating large language models trained on code, comparing performance across tasks like code completion, bug detection, and code summarization. The study assesses model performance on a range of programming languages and finds that larger models generally perform better but with diminishing returns. You can use these results to inform your model selection for code-related tasks.
Key takeaways
- Larger models show better performance on code tasks but with diminishing returns.
- Evaluated across multiple programming languages.
- Useful for builders selecting models for code-related tasks.
OpenAI published a study evaluating large language models trained on code, comparing performance across tasks like code completion, bug detection, and code summarization. The study assesses model performance on a range of programming languages and finds that larger models generally perform better but with diminishing returns. You can use these results to inform your model selection for code-related tasks.
Key takeaways
- Larger models show better performance on code tasks but with diminishing returns.
- Evaluated across multiple programming languages.
- Useful for builders selecting models for code-related tasks.