To what extent does instruction length in BigCodeBench correlate with syntax error rates in generated code for

Abstract

Abstract: Chain-of-thought (CoT) has emerged as a groundbreaking tool in NLP, notably for its efficacy in complex reasoning tasks, such as mathematical proofs. However, its application in code generation faces a distinct challenge, i.e., although the code generated with CoT reasoning is logically correct, it faces the problem of syntax error (e.g., invalid syntax error report) during code execution, which causes the CoT result's pass@1 in HumanEval even lower than the zero-shot result. In this paper, we present Code Chain-of-Thought (CodeCoT) that integrates CoT with a self-examination process for cod

Research Question

To what extent does instruction length in BigCodeBench correlate with syntax error rates in generated code for pandas versus scikit-learn specific tasks?

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