一句话：基于5069个Python编程题样本与13种提示变体，实证发现现有AIGC检测器难以区分人类与ChatGPT生成的代码。
教育者日益担忧ChatGPT等大语言模型在编程教育中被滥用，尤其是学生可能利用AIGC检测器的缺陷实施学术不端。本文开展实证研究，考察LLM能否绕过AIGC检测器。作者构建了一个包含5069个样本的数据集，每个样本由编程问题的文本描述和对应的人类编写Python解答组成，样本来源包括Quescol（80）、Kaggle（3264）和LeetCode（1725）。基于该数据集，作者设计了13组代码问题变体提示，用于指示ChatGPT生成代码输出。随后，作者评估了五种AIGC检测器的性能。结果表明，现有AIGC检测器在区分人类编写代码与AI生成代码方面表现不佳。该研究揭示了当前检测工具在编程教育场景中的局限性，并为教育者、工具开发者和政策制定者提供了警示。
Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of imperfections in Artificial Intelligence Generated Content (AIGC) Detectors for academic misconduct. In this paper, we present an empirical study where the LLM is examined for its attempts to bypass detection by AIGC Detectors. This is achieved by generating code in response to a given question using different variants. We collected a dataset comprising 5,069 samples, with each sample consisting of a textual description of a coding problem and its corresponding human-written Python solution codes. These samples were obtained from various sources, including 80 from Quescol, 3,264 from Kaggle, and 1,725 from Leet-Code. From the dataset, we created 13 sets of code problem variant prompts, which were used to instruct ChatGPT to generate the outputs. Subsequently, we assessed the performance of five AIGC detectors. Our results demonstrate that existing AIGC Detectors perform poorly in distinguishing between human-written code and AI-generated code.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{PanCWSP0C0L24,
title = {Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education},
author = {Wei Hung Pan and Ming Jie Chok and Jonathan Leong Shan Wong and Yung Xin Shin and Yeong Shian Poon and Zhou Yang and Chun Yong Chong and David Lo and Mei Kuan Lim},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3639474.3640068}
}