To what extent does instruction complexity in BigCodeBench correlate with the performance degradation of code

Abstract

Abstract: Large Language Models (LLMs) have garnered remarkable advancements across diverse code-related tasks, known as Code LLMs, particularly in code generation that generates source code with LLM from natural language descriptions. This burgeoning field has captured significant interest from both academic researchers and industry professionals due to its practical significance in software development, e.g., GitHub Copilot. Despite the active exploration of LLMs for a variety of code tasks, either from the perspective of natural language processing (NLP) or software engineering (SE) or both, there is

Research Question

To what extent does instruction complexity in BigCodeBench correlate with the performance degradation of code generation models across different Python data science libraries?

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