计算LLM辅助源代码的原创性
Calculating Originality of LLM Assisted Source Code
AI总结：
本文针对LLM辅助编程中的原创性判定问题，提出一种基于神经网络并受Kolmogorov复杂度启发的工具，用于量化学生在源代码中的原创努力与LLM贡献，初步实验验证了其有效性。
摘要：
使用大型语言模型（LLM）回答各种查询的便捷性及其高可用性，已使LLM被集成到各种应用中。基于LLM的推荐器现在被学生以及专业软件程序员常规地用于代码生成和测试。尽管基于LLM的技术已被证明是有用的，但学生和专业人士对其不道德且未注明出处的使用正日益引发担忧。因此，需要一些工具和技术来帮助教师和其他评估者识别源代码的任何部分是否由LLM生成。在本文中，我们提出了一种基于神经网络的工具，教师可以使用该工具来确定学生在编写源代码时所付出的原创努力（以及LLM的贡献）。我们的工具受到最小描述长度度量（如Kolmogorov复杂度）的启发。我们对中等规模（最多500行代码）的初步实验已显示出有希望的结果，我们在本文中报告了这些结果。
英文摘要：
The ease of using a Large Language Model (LLM) to answer a wide variety of queries and their high availability has resulted in LLMs getting integrated into various applications. LLM-based recommenders are now routinely used by students as well as professional software programmers for code generation and testing. Though LLM-based technology has proven useful, its unethical and unattributed use by students and professionals is a growing cause of concern. As such, there is a need for tools and technologies which may assist teachers and other evaluators in identifying whether any portion of a source code is LLM generated. In this paper, we propose a neural network-based tool that instructors can use to determine the original effort (and LLM's contribution) put by students in writing source codes. Our tool is motivated by minimum description length measures like Kolmogorov complexity. Our initial experiments with moderate sized (up to 500 lines of code) have shown promising results that we report in this paper.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
