핵심 개념
The author proposes CatCode, a framework based on category theory, to comprehensively assess the coding abilities of Large Language Models (LLMs) in understanding and generating a mixture of code and text.
초록
The content introduces CatCode, an evaluation framework based on category theory, to assess LLMs' coding abilities. It addresses challenges in current evaluation methods by proposing a standardized approach that supports diverse task definitions. The framework is applied to morphism identification, translation functors between programming languages, and explanation functors/reproduction functors between code and natural language categories. Results show that models like ChatGPT outperform others in translation tasks but struggle with maintaining functional equivalence between code and explanations.
Key points:
- Introduction of CatCode framework based on category theory for evaluating LLMs.
- Addressing limitations in current evaluation methods with a standardized approach.
- Application of CatCode to morphism identification, translation functors, and explanation/reproduction functors.
- Results showing ChatGPT's superiority in translation tasks but challenges in maintaining functional equivalence.
통계
Large language models such as ChatGPT are proficient in understanding and generating a mixture of code and text.
Evaluation based on this mixture can provide insights into the models' abilities in solving coding problems.
Current evaluation methods lack standardization or comprehensive coverage of tasks.
Category theory is proposed as a framework for evaluation to address these issues.
The CatCode framework aims to comprehensively assess the coding abilities of LLMs using morphisms within code categories.