@inproceedings{lekkala-etal-2025-nits,
title = "nits{\_}teja{\_}srikar at {G}en{AI} Detection Task 2: Distinguishing Human and {AI}-Generated Essays Using Machine Learning and Transformer Models",
author = "Lekkala, Sai Teja and
Yadagiri, Annepaka and
Vardhan, Mangadoddi Srikar and
Pakray, Partha",
editor = "Alam, Firoj and
Nakov, Preslav and
Habash, Nizar and
Gurevych, Iryna and
Chowdhury, Shammur and
Shelmanov, Artem and
Wang, Yuxia and
Artemova, Ekaterina and
Kutlu, Mucahid and
Mikros, George",
booktitle = "Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "International Conference on Computational Linguistics",
url = "https://aclanthology.org/2025.genaidetect-1.30/",
pages = "278--283",
abstract = "This paper presents models to differentiate between human-written and AI-generated essays, addressing challenges posed by advanced AI models like ChatGPT and Claude. Using a structured dataset, we fine-tune multiple machine learning models, including XGBoost and Logistic Regression, along with ensemble learning and k-fold cross-validation. The dataset is processed through TF-IDF vectorization, followed by text cleaning, lemmatization, stemming, and part-of-speech tagging before training. Our team nits{\_}teja{\_}srikar achieves high accuracy, with DistilBERT performing at 77.3{\%} accuracy, standing at 20th position for English, and XLM-RoBERTa excelling in Arabic at 92.2{\%}, standing at 14th position in the official leaderboard, demonstrating the model`s potential for real-world applications."
}
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%0 Conference Proceedings
%T nits_teja_srikar at GenAI Detection Task 2: Distinguishing Human and AI-Generated Essays Using Machine Learning and Transformer Models
%A Lekkala, Sai Teja
%A Yadagiri, Annepaka
%A Vardhan, Mangadoddi Srikar
%A Pakray, Partha
%Y Alam, Firoj
%Y Nakov, Preslav
%Y Habash, Nizar
%Y Gurevych, Iryna
%Y Chowdhury, Shammur
%Y Shelmanov, Artem
%Y Wang, Yuxia
%Y Artemova, Ekaterina
%Y Kutlu, Mucahid
%Y Mikros, George
%S Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)
%D 2025
%8 January
%I International Conference on Computational Linguistics
%C Abu Dhabi, UAE
%F lekkala-etal-2025-nits
%X This paper presents models to differentiate between human-written and AI-generated essays, addressing challenges posed by advanced AI models like ChatGPT and Claude. Using a structured dataset, we fine-tune multiple machine learning models, including XGBoost and Logistic Regression, along with ensemble learning and k-fold cross-validation. The dataset is processed through TF-IDF vectorization, followed by text cleaning, lemmatization, stemming, and part-of-speech tagging before training. Our team nits_teja_srikar achieves high accuracy, with DistilBERT performing at 77.3% accuracy, standing at 20th position for English, and XLM-RoBERTa excelling in Arabic at 92.2%, standing at 14th position in the official leaderboard, demonstrating the model‘s potential for real-world applications.
%U https://aclanthology.org/2025.genaidetect-1.30/
%P 278-283
Markdown (Informal)
[nits_teja_srikar at GenAI Detection Task 2: Distinguishing Human and AI-Generated Essays Using Machine Learning and Transformer Models](https://aclanthology.org/2025.genaidetect-1.30/) (Lekkala et al., GenAIDetect 2025)
ACL