Search Field
2024
Oedingen, Marc; Engelhardt, Raphael C.; Denz, Robin; Hammer, Maximilian; Konen, Wolfgang
ChatGPT Code Detection: Techniques for Uncovering the Source of Code Artikel
In: arXiv preprint arXiv:2405.15512, 2024.
@article{Oedingen2024,
title = {ChatGPT Code Detection: Techniques for Uncovering the Source of Code},
author = {Marc Oedingen and Raphael C. Engelhardt and Robin Denz and Maximilian Hammer and Wolfgang Konen},
url = {https://arxiv.org/abs/2405.15512},
year = {2024},
date = {2024-01-01},
journal = {arXiv preprint arXiv:2405.15512},
keywords = {AI, ChatGPT, Code Detection, Large Language Models, machine learning},
pubstate = {published},
tppubtype = {article}
}
Oedingen, Marc; Engelhardt, Raphael C.; Denz, Robin; Hammer, Maximilian; Konen, Wolfgang
ChatGPT Code Detection: Techniques for Uncovering the Source of Code Artikel
In: AI, Bd. 5, Nr. 3, S. 1066–1094, 2024, ISSN: 2673-2688.
@article{Oedingen2024a,
title = {ChatGPT Code Detection: Techniques for Uncovering the Source of Code},
author = {Marc Oedingen and Raphael C. Engelhardt and Robin Denz and Maximilian Hammer and Wolfgang Konen},
url = {https://www.mdpi.com/2673-2688/5/3/53},
doi = {10.3390/ai5030053},
issn = {2673-2688},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {AI},
volume = {5},
number = {3},
pages = {1066–1094},
abstract = {In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas such as higher education. The present paper explores this issue by using advanced classification techniques to differentiate between code written by humans and code generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful embedding features (black-box) with supervised learning algorithms including Deep Neural Networks, Random Forests, and Extreme Gradient Boosting to achieve this differentiation with an impressive accuracy of 98%. For the successful combinations, we also examine their model calibration, showing that some of the models are extremely well calibrated. Additionally, we present white-box features and an interpretable Bayes classifier to elucidate critical differences between the code sources, enhancing the explainability and transparency of our approach. Both approaches work well, but provide at most 85–88% accuracy. Tests on a small sample of untrained humans suggest that humans do not solve the task much better than random guessing. This study is crucial in understanding and mitigating the potential risks associated with using AI in code generation, particularly in the context of higher education, software development, and competitive programming.},
keywords = {AI, ChatGPT, Code Detection, Large Language Models, machine learning},
pubstate = {published},
tppubtype = {article}
}
In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas such as higher education. The present paper explores this issue by using advanced classification techniques to differentiate between code written by humans and code generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful embedding features (black-box) with supervised learning algorithms including Deep Neural Networks, Random Forests, and Extreme Gradient Boosting to achieve this differentiation with an impressive accuracy of 98%. For the successful combinations, we also examine their model calibration, showing that some of the models are extremely well calibrated. Additionally, we present white-box features and an interpretable Bayes classifier to elucidate critical differences between the code sources, enhancing the explainability and transparency of our approach. Both approaches work well, but provide at most 85–88% accuracy. Tests on a small sample of untrained humans suggest that humans do not solve the task much better than random guessing. This study is crucial in understanding and mitigating the potential risks associated with using AI in code generation, particularly in the context of higher education, software development, and competitive programming.
2021
Meissner, Simon
Untersuchung des Spiel- und Lernerfolgs künstlicher Intelligenzen für ein nichtdeterministisches Spiel mit imperfekten Informationen: Blackjack in der Game-Learning-Umgebung ’General Board Game’ (GBG) Abschlussarbeit
TH Köln – University of Applied Sciences, 2021, (Bachelor thesis).
@mastersthesis{Meissner2021,
title = {Untersuchung des Spiel- und Lernerfolgs künstlicher Intelligenzen für ein nichtdeterministisches Spiel mit imperfekten Informationen: Blackjack in der Game-Learning-Umgebung ’General Board Game’ (GBG)},
author = {Simon Meissner},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA-Meissner-final-2021.pdf},
year = {2021},
date = {2021-12-01},
school = {TH Köln – University of Applied Sciences},
note = {Bachelor thesis},
keywords = {AI, BT-MT, Game Learning, GBG, machine learning},
pubstate = {published},
tppubtype = {mastersthesis}
}
Zeh, Tim
Untersuchung von allgemeinen KI-Agenten für das Spiel Poker im General Board Games Framework Abschlussarbeit
TH Köln – University of Applied Sciences, 2021, (Master thesis).
@mastersthesis{Zeh2021,
title = {Untersuchung von allgemeinen KI-Agenten für das Spiel Poker im General Board Games Framework},
author = {Tim Zeh},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/MA_Zeh_final_Poker-GBG-2021.pdf},
year = {2021},
date = {2021-07-01},
school = {TH Köln – University of Applied Sciences},
note = {Master thesis},
keywords = {AI, BT-MT, Game Learning, GBG, machine learning},
pubstate = {published},
tppubtype = {mastersthesis}
}
2020
Bagheri, Samineh
Self-Adjusting Surrogate-Assisted Optimization Techniques for Expensive Constrained Black Box Problems Promotionsarbeit
Leiden University and TH Köln, 2020, (PhD thesis).
@phdthesis{Bagheri2020,
title = {Self-Adjusting Surrogate-Assisted Optimization Techniques for Expensive Constrained Black Box Problems},
author = {Samineh Bagheri},
year = {2020},
date = {2020-04-01},
institution = {Institut für Informatik},
school = {Leiden University and TH Köln},
note = {PhD thesis},
keywords = {BT-MT, machine learning, MONREP, optimization, RBF, SACOBRA, surrogate models},
pubstate = {published},
tppubtype = {phdthesis}
}
Scheiermann, Johannes
Sind (trainierte) General-Purpose-RL-Agenten im Brettspiel Othello stärker als (untrainierte) General-Game-Playing Agenten? Forschungsbericht
TH Köln, Institut für Informatik 2020, (Praxisprojekt).
@techreport{Scheier2020,
title = {Sind (trainierte) General-Purpose-RL-Agenten im Brettspiel Othello stärker als (untrainierte) General-Game-Playing Agenten?},
author = {Johannes Scheiermann},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/INF-Prj-Scheiermann-2020-08.pdf},
year = {2020},
date = {2020-01-01},
institution = {TH Köln, Institut für Informatik},
note = {Praxisprojekt},
keywords = {AI, BT-MT, Game Learning, GBG, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
Scheiermann, Johannes
AlphaZero-inspirierte KI-Agenten im General Board Game Playing Abschlussarbeit
TH Köln -- University of Applied Sciences, 2020, (Bachelor thesis).
@mastersthesis{Scheier2020b,
title = {AlphaZero-inspirierte KI-Agenten im General Board Game Playing},
author = {Johannes Scheiermann},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA_Scheiermann_final.pdf},
year = {2020},
date = {2020-01-01},
school = {TH Köln -- University of Applied Sciences},
note = {Bachelor thesis},
keywords = {AI, BT-MT, Game Learning, GBG, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {mastersthesis}
}
2019
Cöln, Julian; Dittmar, Yannick
Untersuchung von KI Agenten im Spiel Othello Forschungsbericht
TH Köln, Institut für Informatik 2019.
@techreport{Cöln2019,
title = {Untersuchung von KI Agenten im Spiel Othello},
author = {Julian Cöln and Yannick Dittmar},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/INF-Proj-DittmarCoeln-2019-12.pdf},
year = {2019},
date = {2019-12-01},
institution = {TH Köln, Institut für Informatik},
keywords = {AI, BT-MT, Game Learning, GBG, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {techreport}
}
Barsnick, Felix
Implementierung und Untersuchung eines Turniersystems für KI-Agenten in Brettspielen Abschlussarbeit
TH Köln -- University of Applied Sciences, 2019, (Master thesis).
@mastersthesis{Barsnick2019,
title = {Implementierung und Untersuchung eines Turniersystems für KI-Agenten in Brettspielen},
author = {Felix Barsnick},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/MA_MMI_Barsnick-2019-04-final.pdf},
year = {2019},
date = {2019-01-01},
institution = {Institut für Informatik},
school = {TH Köln -- University of Applied Sciences},
note = {Master thesis},
keywords = {BT-MT, Elo, Game Learning, GBG, Glicko, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {mastersthesis}
}
2015
Koch, Patrick; Wagner, Tobias; Emmerich, Michael; Bäck, Thomas; Konen, Wolfgang
Efficient multi-criteria optimization on noisy machine learning problems Artikel
In: Applied Soft Computing, Bd. 29, S. 357-370, 2015.
@article{Koch15,
title = {Efficient multi-criteria optimization on noisy machine learning problems},
author = {Patrick Koch and Tobias Wagner and Michael Emmerich and Thomas Bäck and Wolfgang Konen},
url = {http://www.sciencedirect.com/science/article/pii/S156849461500006X#},
doi = {https://doi.org/10.1016/j.asoc.2015.01.005},
year = {2015},
date = {2015-05-01},
journal = {Applied Soft Computing},
volume = {29},
pages = {357-370},
keywords = {machine learning, TDMR},
pubstate = {published},
tppubtype = {article}
}
2011
Konen, Wolfgang
Der SFA-Algorithmus für Klassifikation Forschungsbericht
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, Nr. 08/11, 2011, ISSN: 2191-365X.
@techreport{Kone11e,
title = {Der SFA-Algorithmus für Klassifikation},
author = {Wolfgang Konen},
url = {http://maanvs03.gm.fh-koeln.de/webpub/CIOPReports.d/Konen11e.d/Konen11e.pdf},
issn = {2191-365X},
year = {2011},
date = {2011-01-01},
number = {08/11},
address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
institution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
keywords = {machine learning, SFA, SOMA},
pubstate = {published},
tppubtype = {techreport}
}
2009
Konen, Wolfgang; Bartz-Beielstein, Thomas
Reinforcement learning for games: failures and successes Proceedings Article
In: GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, S. 2641–2648, ACM, Montreal, Québec, Canada, 2009.
@inproceedings{Kone09ab,
title = {Reinforcement learning for games: failures and successes},
author = {Wolfgang Konen and Thomas Bartz-Beielstein},
year = {2009},
date = {2009-01-01},
booktitle = {GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference},
pages = {2641--2648},
publisher = {ACM},
address = {Montreal, Québec, Canada},
keywords = {games, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
2008
Konen, Wolfgang; Bartz-Beielstein, Thomas
Reinforcement Learning: Insights from Interesting Failures in Parameter Selection Proceedings Article
In: and, Günter Rudolph (Hrsg.): PPSN'2008: 10th International Conference on Parallel Problem Solving From Nature, Dortmund, S. 478–487, Springer, Berlin, 2008.
@inproceedings{Kone08ab,
title = {Reinforcement Learning: Insights from Interesting Failures in Parameter Selection},
author = {Wolfgang Konen and Thomas Bartz-Beielstein},
editor = {Günter Rudolph and et al.},
year = {2008},
date = {2008-01-01},
booktitle = {PPSN'2008: 10th International Conference on Parallel Problem Solving From Nature, Dortmund},
pages = {478--487},
publisher = {Springer},
address = {Berlin},
keywords = {learning, machine learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Search Field
13 Einträge « ‹ 1 von 2
› » 1.
Oedingen, Marc; Engelhardt, Raphael C.; Denz, Robin; Hammer, Maximilian; Konen, Wolfgang
ChatGPT Code Detection: Techniques for Uncovering the Source of Code Artikel
In: arXiv preprint arXiv:2405.15512, 2024.
@article{Oedingen2024,
title = {ChatGPT Code Detection: Techniques for Uncovering the Source of Code},
author = {Marc Oedingen and Raphael C. Engelhardt and Robin Denz and Maximilian Hammer and Wolfgang Konen},
url = {https://arxiv.org/abs/2405.15512},
year = {2024},
date = {2024-01-01},
journal = {arXiv preprint arXiv:2405.15512},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2.
Oedingen, Marc; Engelhardt, Raphael C.; Denz, Robin; Hammer, Maximilian; Konen, Wolfgang
ChatGPT Code Detection: Techniques for Uncovering the Source of Code Artikel
In: AI, Bd. 5, Nr. 3, S. 1066–1094, 2024, ISSN: 2673-2688.
@article{Oedingen2024a,
title = {ChatGPT Code Detection: Techniques for Uncovering the Source of Code},
author = {Marc Oedingen and Raphael C. Engelhardt and Robin Denz and Maximilian Hammer and Wolfgang Konen},
url = {https://www.mdpi.com/2673-2688/5/3/53},
doi = {10.3390/ai5030053},
issn = {2673-2688},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {AI},
volume = {5},
number = {3},
pages = {1066–1094},
abstract = {In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas such as higher education. The present paper explores this issue by using advanced classification techniques to differentiate between code written by humans and code generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful embedding features (black-box) with supervised learning algorithms including Deep Neural Networks, Random Forests, and Extreme Gradient Boosting to achieve this differentiation with an impressive accuracy of 98%. For the successful combinations, we also examine their model calibration, showing that some of the models are extremely well calibrated. Additionally, we present white-box features and an interpretable Bayes classifier to elucidate critical differences between the code sources, enhancing the explainability and transparency of our approach. Both approaches work well, but provide at most 85–88% accuracy. Tests on a small sample of untrained humans suggest that humans do not solve the task much better than random guessing. This study is crucial in understanding and mitigating the potential risks associated with using AI in code generation, particularly in the context of higher education, software development, and competitive programming.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas such as higher education. The present paper explores this issue by using advanced classification techniques to differentiate between code written by humans and code generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful embedding features (black-box) with supervised learning algorithms including Deep Neural Networks, Random Forests, and Extreme Gradient Boosting to achieve this differentiation with an impressive accuracy of 98%. For the successful combinations, we also examine their model calibration, showing that some of the models are extremely well calibrated. Additionally, we present white-box features and an interpretable Bayes classifier to elucidate critical differences between the code sources, enhancing the explainability and transparency of our approach. Both approaches work well, but provide at most 85–88% accuracy. Tests on a small sample of untrained humans suggest that humans do not solve the task much better than random guessing. This study is crucial in understanding and mitigating the potential risks associated with using AI in code generation, particularly in the context of higher education, software development, and competitive programming.
3.
Meissner, Simon
Untersuchung des Spiel- und Lernerfolgs künstlicher Intelligenzen für ein nichtdeterministisches Spiel mit imperfekten Informationen: Blackjack in der Game-Learning-Umgebung ’General Board Game’ (GBG) Abschlussarbeit
TH Köln – University of Applied Sciences, 2021, (Bachelor thesis).
@mastersthesis{Meissner2021,
title = {Untersuchung des Spiel- und Lernerfolgs künstlicher Intelligenzen für ein nichtdeterministisches Spiel mit imperfekten Informationen: Blackjack in der Game-Learning-Umgebung ’General Board Game’ (GBG)},
author = {Simon Meissner},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA-Meissner-final-2021.pdf},
year = {2021},
date = {2021-12-01},
school = {TH Köln – University of Applied Sciences},
note = {Bachelor thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
4.
Zeh, Tim
Untersuchung von allgemeinen KI-Agenten für das Spiel Poker im General Board Games Framework Abschlussarbeit
TH Köln – University of Applied Sciences, 2021, (Master thesis).
@mastersthesis{Zeh2021,
title = {Untersuchung von allgemeinen KI-Agenten für das Spiel Poker im General Board Games Framework},
author = {Tim Zeh},
url = {https://www.gm.fh-koeln.de/~konen/research/PaperPDF/MA_Zeh_final_Poker-GBG-2021.pdf},
year = {2021},
date = {2021-07-01},
school = {TH Köln – University of Applied Sciences},
note = {Master thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
5.
Bagheri, Samineh
Self-Adjusting Surrogate-Assisted Optimization Techniques for Expensive Constrained Black Box Problems Promotionsarbeit
Leiden University and TH Köln, 2020, (PhD thesis).
@phdthesis{Bagheri2020,
title = {Self-Adjusting Surrogate-Assisted Optimization Techniques for Expensive Constrained Black Box Problems},
author = {Samineh Bagheri},
year = {2020},
date = {2020-04-01},
institution = {Institut für Informatik},
school = {Leiden University and TH Köln},
note = {PhD thesis},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
6.
Scheiermann, Johannes
Sind (trainierte) General-Purpose-RL-Agenten im Brettspiel Othello stärker als (untrainierte) General-Game-Playing Agenten? Forschungsbericht
TH Köln, Institut für Informatik 2020, (Praxisprojekt).
@techreport{Scheier2020,
title = {Sind (trainierte) General-Purpose-RL-Agenten im Brettspiel Othello stärker als (untrainierte) General-Game-Playing Agenten?},
author = {Johannes Scheiermann},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/INF-Prj-Scheiermann-2020-08.pdf},
year = {2020},
date = {2020-01-01},
institution = {TH Köln, Institut für Informatik},
note = {Praxisprojekt},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
7.
Scheiermann, Johannes
AlphaZero-inspirierte KI-Agenten im General Board Game Playing Abschlussarbeit
TH Köln -- University of Applied Sciences, 2020, (Bachelor thesis).
@mastersthesis{Scheier2020b,
title = {AlphaZero-inspirierte KI-Agenten im General Board Game Playing},
author = {Johannes Scheiermann},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/BA_Scheiermann_final.pdf},
year = {2020},
date = {2020-01-01},
school = {TH Köln -- University of Applied Sciences},
note = {Bachelor thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
8.
Cöln, Julian; Dittmar, Yannick
Untersuchung von KI Agenten im Spiel Othello Forschungsbericht
TH Köln, Institut für Informatik 2019.
@techreport{Cöln2019,
title = {Untersuchung von KI Agenten im Spiel Othello},
author = {Julian Cöln and Yannick Dittmar},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/INF-Proj-DittmarCoeln-2019-12.pdf},
year = {2019},
date = {2019-12-01},
institution = {TH Köln, Institut für Informatik},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
9.
Barsnick, Felix
Implementierung und Untersuchung eines Turniersystems für KI-Agenten in Brettspielen Abschlussarbeit
TH Köln -- University of Applied Sciences, 2019, (Master thesis).
@mastersthesis{Barsnick2019,
title = {Implementierung und Untersuchung eines Turniersystems für KI-Agenten in Brettspielen},
author = {Felix Barsnick},
url = {http://www.gm.fh-koeln.de/~konen/research/PaperPDF/MA_MMI_Barsnick-2019-04-final.pdf},
year = {2019},
date = {2019-01-01},
institution = {Institut für Informatik},
school = {TH Köln -- University of Applied Sciences},
note = {Master thesis},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
10.
Koch, Patrick; Wagner, Tobias; Emmerich, Michael; Bäck, Thomas; Konen, Wolfgang
Efficient multi-criteria optimization on noisy machine learning problems Artikel
In: Applied Soft Computing, Bd. 29, S. 357-370, 2015.
@article{Koch15,
title = {Efficient multi-criteria optimization on noisy machine learning problems},
author = {Patrick Koch and Tobias Wagner and Michael Emmerich and Thomas Bäck and Wolfgang Konen},
url = {http://www.sciencedirect.com/science/article/pii/S156849461500006X#},
doi = {https://doi.org/10.1016/j.asoc.2015.01.005},
year = {2015},
date = {2015-05-01},
journal = {Applied Soft Computing},
volume = {29},
pages = {357-370},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
13 Einträge « ‹ 1 von 2
› »
