Tesla at GenAI Detection Task 2: Fast and Scalable Method for Detection of Academic Essay Authenticity

Vijayasaradhi Indurthi, Vasudeva Varma


Abstract
This paper describes a simple yet effective method to identify if academic essays have been written by students or generated through the language models in English language. We extract a set of style, language complexity, bias and subjectivity, and emotion-based features that can be used to distinguish human-written essays from machine-generated essays. Our methods rank 6th on the leaderboard, achieving an impressive F1-score of 0.986.
Anthology ID:
2025.genaidetect-1.36
Volume:
Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)
Month:
January
Year:
2025
Address:
Abu Dhabi, UAE
Editors:
Firoj Alam, Preslav Nakov, Nizar Habash, Iryna Gurevych, Shammur Chowdhury, Artem Shelmanov, Yuxia Wang, Ekaterina Artemova, Mucahid Kutlu, George Mikros
Venues:
GenAIDetect | WS
SIG:
Publisher:
International Conference on Computational Linguistics
Note:
Pages:
317–322
Language:
URL:
https://aclanthology.org/2025.genaidetect-1.36/
DOI:
Bibkey:
Cite (ACL):
Vijayasaradhi Indurthi and Vasudeva Varma. 2025. Tesla at GenAI Detection Task 2: Fast and Scalable Method for Detection of Academic Essay Authenticity. In Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect), pages 317–322, Abu Dhabi, UAE. International Conference on Computational Linguistics.
Cite (Informal):
Tesla at GenAI Detection Task 2: Fast and Scalable Method for Detection of Academic Essay Authenticity (Indurthi & Varma, GenAIDetect 2025)
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PDF:
https://aclanthology.org/2025.genaidetect-1.36.pdf