nits_teja_srikar at G en AI Detection Task 2: Distinguishing Human and AI -Generated Essays Using Machine Learning and Transformer Models (2025)

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.

## introduction generative artificial intelligence (ai) tools @xcite are revolutionizing the creation of text, images, and videos, reshaping how society consumes and produces online content. as these technologies advance, distinguishing between aigenerated and human-generated content has become increasingly challenging. ai-generated content (aigc) @xcite spans a broad range of media, including text, code, images, and music, with rapidly expanding applications in areas such as news reports, blog posts, scriptwriting, and marketing copy. a study published in the public library of science (plos) found that readers were more likely to agree with arguments in ai-generated essays than those in human-written ones @xcite . these findings underscore the increasing need for models reliably differentiating between ai-generated and human-authored content. in this paper, we employ a fine-tuning approach utiliz-ing multiple machine learning (ml) models, including xgboost and logistic regression (google-research), along with their k-fold cross-validation variants @xcite , and an ensemble learning method. a hybrid model integrating all models and their k-fold variants was developed using ensemble learning @xcite and transformer models using distilbert @xcite and xlm-roberta @xcite to further improve detection accuracy in distinguishing between human and ai-generated essays. ## related work liao et al. @xcite proposed an ethical framework for artificial intelligence generated content (aigc) within the healthcare sector. this study investigated the differences between medical texts authored by chatgpt and those written by healthcare professionals. additionally, the authors developed machine learning workflows aimed at identifying and distinguishing medical texts generated by chatgpt. to achieve this, they curated datasets, with one dataset consisting of chatgptgenerated medical texts and the other containing texts authored by human experts. subsequently, they implemented ml methods to determine the source of the medical text content. alamleh et al. @xcite assessed the effectiveness of various ml methods in differentiating ai-generated text from text authored by humans. to carry out this analysis, they collected responses from computer science students to both essay and programming assignments. using this dataset, they trained and evaluated multiple machine learning models, including support vector machines (svm), logistic regression (lr), neural networks (nn), random forest (rf), and decision trees (dt). chen et al. @xcite proposed a novel approach to distinguish between texts written by humans and those generated by chatgpt using language-based techniques. the researchers collected and released a curated dataset named opengpttext, comprising rephrased content created through chatgpt. ## methodology we leverage both traditional machine-learning models and advanced transformer-based models. below, we provide a detailed technical description of our methodology for both datasets. ## results the distilbert model excels in the english dataset, while xlm-roberta performs well in arabic. the confusion matrix highlights their accuracy and offers insights into their classification performance. ## conclusion our study demonstrates the efficacy of various ml and transformer-based models in distinguishing between human-generated and ai-generated essays in both english and arabic. utilizing models like distilbert and xlm-roberta, we achieved superior detection precisions of 0.875 for english and a strong performance of 0.943 for arabic, as shown in references hosam alamleh, ali abdullah s alqahtani, and abdel-rahman elsaid. 2023. distinguishing human-written and chatgpt-generated text using machine learning. in 2023 systems and information engineering design symposium (sieds), pages 154-158. ieee. p. m. bal and m. veltkamp. 2013. how does fiction reading influence empathy? an experimental investi-label count word sentence unique human 1145 241.51 8.71 169.54 ai 925 200.96 14.33 137.57 (a) comparison of textual metrics in number counts (b) train data essay length distribution figure 2: train data visualization for arabic gation on the role of emotional transportation. plos one, 8(1):e55341. a. j. bowers and x. zhou. 2019. receiver operating characteristic (roc) area under the curve (auc): a diagnostic measure for evaluating the accuracy of predictors of education outcomes. journal of education for students placed at risk (jespar), 24(1):20-46.