Summary of Chatgpt Code Detection: Techniques For Uncovering the Source Of Code, by Marc Oedingen et al.
ChatGPT Code Detection: Techniques for Uncovering the Source of Code
by Marc Oedingen, Raphael C. Engelhardt, Robin Denz, Maximilian Hammer, Wolfgang Konen
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the influence of large language models (LLMs) on computer code generation, examining how to differentiate between human-written code and code produced by ChatGPT. The authors employ advanced classification techniques, combining powerful embedding features with supervised learning algorithms like Deep Neural Networks, Random Forests, and Extreme Gradient Boosting. This approach achieves an impressive accuracy of 98%. The study also examines model calibration, presenting white-box features and an interpretable Bayes classifier to elucidate critical differences between code sources. Additionally, the authors show that untrained humans solve the same task not better than random guessing. The paper highlights the importance of understanding and mitigating potential risks associated with using AI in code generation, particularly in higher education, software development, and competitive programming. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how artificial intelligence can help generate computer code, which is important because it could be used to cheat on school assignments or other tasks. The researchers use special techniques to try to tell apart code that was written by a person from code that was generated by a large language model called ChatGPT. They found that their approach worked well, but not perfectly – it got about 98% of the answers correct. The study also looked at why some of the models were better than others and how untrained people would do on this task. Overall, the research is important because it helps us understand the potential risks and benefits of using AI to generate code. |
Keywords
» Artificial intelligence » Classification » Embedding » Extreme gradient boosting » Large language model » Supervised
