The article presents COCOGEN, a method for representing commonsense knowledge through code, enhancing code generation tasks. It details the conversion of commonsense structures into Python and evaluates performance using variants of OpenAI's CODEX models. Key findings indicate that larger models significantly outperform smaller ones, emphasizing the importance of model capabilities in generating effective prompts. Different experimental setups highlight the impact of prompt variations on sensitivity and performance, suggesting that optimized prompts can lead to superior outcomes in code generation tasks.
In our study, we explore the effectiveness of COCOGEN, which employs few-shot prompting to leverage commonsense structures for code generation, showcasing it as a promising approach to enhance programming efficiency.
The comparison between COCOGEN models using code-davinci-001 and code-davinci-002 reveals a substantial performance difference, underscoring the significance of model size and complexity in code generation tasks.
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