ConCodeEval: Evaluating Large Language Models for Code Constraints in Domain-Specific Languages
This addresses a crucial gap for enterprises using LLMs for system-level programming, as it is the first benchmark to evaluate controllability over code constraints, though it is incremental in focusing on a specific domain.
The paper tackles the problem of evaluating whether large language models (LLMs) can comprehend code constraints in domain-specific languages (DSLs) like JSON and YAML, and finds that LLMs struggle with these constraints despite performing well on normal code tasks.
Recent work shows Large Language Models (LLMs) struggle to understand natural language constraints for various text generation tasks in zero- and few-shot settings. While, in the code domain, there is wide usage of constraints in code format to maintain the integrity of code written in Domain-Specific Languages (DSLs) like JSON and YAML which are widely used for system-level programming tasks in enterprises. Given that LLMs are increasingly used for system-level code tasks, evaluating if they can comprehend these code constraints is crucial. However, no work has been done to evaluate their controllability over code constraints. Hence, we introduce ConCodeEval, a first-of-its-kind benchmark having two novel tasks for code constraints across five representations. Our findings suggest that language models struggle with code constraints. Code languages that perform excellently for normal code tasks do not perform well when the same languages represent fine-grained constraints.