ymoslem/acl-abstracts huggingface dataset:
Fraunhofer SIT at GenAI Detection Task 1: Adapter Fusion for AI-generated Text Detection (2025)

The detection of AI-generated content is becoming increasingly important with the growing prevalence of tools such as ChatGPT. This paper presents our results in the GenAI Content Detection Task 1, focusing on binary English and multilingual AI-generated text detection. We trained and tested transformers, adapters and adapter fusion. In the English setting (Subtask A), the combination of our own adapter on AI-generated text detection based on RoBERTa with a task adapter on multi-genre NLI yielded a macro F1 score of 0.828 on the challenge test set, ranking us third out of 35 teams. In the multilingual setting (Subtask B), adapter fusion resulted in a deterioration of the results. Consequently, XLM-RoBERTa, fine-tuned on the training set, was employed for the final evaluation, attaining a macro F1 score of 0.7258 and ranking tenth out of 25 teams.
OSINT at GenAI Detection Task 1: Multilingual MGT Detection: Leveraging Cross-Lingual Adaptation for Robust LLMs Text Identification (2025)

Detecting AI-generated text has become in- creasingly prominent. This paper presents our solution for the DAIGenC Task 1 Subtask 2, where we address the challenge of distin- guishing human-authored text from machine- generated content, especially in multilingual contexts. We introduce Multi-Task Detection (MLDet), a model that leverages Cross-Lingual Adaptation and Model Generalization strate- gies for Multilingual Machine-Generated Text (MGT) detection. By combining language- specific embeddings with fusion techniques, MLDet creates a unified, language-agnostic feature representation, enhancing its ability to generalize across diverse languages and mod- els. Our approach demonstrates strong perfor- mance, achieving macro and micro F1 scores of 0.7067 and 0.7187, respectively, and ranking 15th in the competition1. We also evaluate our model across datasets generated by different distinct models in many languages, showcasing its robustness in multilingual and cross-model scenarios.
Nota AI at GenAI Detection Task 1: Unseen Language-Aware Detection System for Multilingual Machine-Generated Text (2025)

Recently, large language models (LLMs) have demonstrated unprecedented capabilities in language generation, yet they still often produce incorrect information. Therefore, determining whether a text was generated by an LLM has become one of the factors that must be considered when evaluating its reliability. In this paper, we discuss methods to determine whether texts written in various languages were authored by humans or generated by LLMs. We have discovered that the classification accuracy significantly decreases for texts written in languages not observed during the training process, and we aim to address this issue. We propose a method to improve performance for unseen languages by using token-level predictive distributions extracted from various LLMs and text embeddings from a multilingual pre-trained langauge model. With the proposed method, we achieved third place out of 25 teams in Subtask B (binary multilingual machine-generated text detection) of Shared Task 1, with an F1 macro score of 0.7532.
CNLP-NITS-PP at GenAI Detection Task 1: AI-Generated Text Using Transformer-Based Approaches (2025)

In the current digital landscape, distinguishing between text generated by humans and that created by large language models has become increasingly complex. This challenge is exacerbated by advanced LLMs such as the Gemini, ChatGPT, GPT-4, and LLaMa, which can produce highly sophisticated, human-like text. This indistinguishability introduces a range of challenges across different sectors. Cybersecurity increases the risk of social engineering and misinformation, while social media aids the spread of biased or false content. The educational sector faces issues of academic integrity, and within large, multi-team environments, these models add complexity to managing interactions between human and AI agents. To address these challenges, we approached the problem as a binary classification task using an English-language benchmark COLING dataset. We employed transformer-based neural network models, including BERT, DistilBERT, and RoBERTa, fine-tuning each model with optimized hyperparameters to maximize classification accuracy. Our team CNLP-NITS-PP has achieved the 23rd rank in subtask 1 at COLING-2025 for machine-generated text detection in English with a Main Score F1 Macro of 0.6502 and micro-F1 score of 0.6876.
LuxVeri at GenAI Detection Task 1: Inverse Perplexity Weighted Ensemble for Robust Detection of AI-Generated Text across English and Multilingual Contexts (2025)

This paper presents a system developed for Task 1 of the COLING 2025 Workshop on Detecting AI-Generated Content, focusing on the binary classification of machine-generated versus human-written text. Our approach utilizes an ensemble of models, with weights assigned according to each model`s inverse perplexity, to enhance classification accuracy. For the English text detection task, we combined RoBERTa-base, RoBERTa-base with the OpenAI detector, and BERT-base-cased, achieving a Macro F1-score of 0.7458, which ranked us 12th out of 35 teams. We ensembled RemBERT, XLM-RoBERTa-base, and BERT-base-multilingual-case for the multilingual text detection task, employing the same inverse perplexity weighting technique. This resulted in a Macro F1-score of 0.7513, positioning us 4th out of 25 teams. Our results demonstrate the effectiveness of inverse perplexity weighting in improving the robustness of machine-generated text detection across both monolingual and multilingual settings, highlighting the potential of ensemble methods for this challenging task.
Grape at GenAI Detection Task 1: Leveraging Compact Models and Linguistic Features for Robust Machine-Generated Text Detection (2025)

In this project, we aim to address two subtasks of Task 1: Binary Multilingual Machine-Generated Text (MGT) Detection (Human vs. Machine) as part of the COLING 2025 Workshop on MGT Detection (Wang et al., 2025) using different approaches. The first method involves separately fine-tuning small language models tailored to the specific subtask. The second approach builds on this methodology by incorporating linguistic, syntactic, and semantic features, leveraging ensemble learning to integrate these features with model predictions for more robust classification. By evaluating and comparing these approaches, we aim to identify the most effective techniques for detecting machine-generated content across languages, providing insights into improving automated verification tools amidst the rapid growth of LLM-generated text in digital spaces.
AAIG at GenAI Detection Task 1: Exploring Syntactically-Aware, Resource-Efficient Small Autoregressive Decoders for AI Content Detection (2025)

This paper presents a lightweight and efficient approach to AI-generated content detection using small autoregressive fine-tuned decoders (AFDs) for secure, on-device deployment. Motivated by resource-efficiency, syntactic awareness, and bias mitigation, our model employs small language models (SLMs) with autoregressive pre-training and loss fusion to accurately distinguish between human and AI-generated content while significantly reducing computational demands. The system achieved highest macro-F1 score of 0.8186, with the submitted model scoring 0.7874{---}both significantly outperforming the task baseline while reducing model parameters by {\textasciitilde}60{\%}. Notably, our approach mitigates biases, improving recall for human-authored text by over 60{\%}. Ranking 8th out of 36 participants, these results confirm the feasibility and competitiveness of small AFDs in challenging, adversarial settings, making them ideal for privacy-preserving, on-device deployment suitable for real-world applications.
TurQUaz at GenAI Detection Task 1:Dr. Perplexity or: How I Learned to Stop Worrying and Love the Finetuning (2025)

This paper details our methods for addressing Task 1 of the GenAI Content Detection shared tasks, which focus on distinguishing AI-generated text from human-written content. The task comprises two subtasks: Subtask A, centered on English-only datasets, and Subtask B, which extends the challenge to multilingual data. Our approach uses a fine-tuned XLM-RoBERTa model for classification, complemented by features including perplexity and TF-IDF. While perplexity is commonly regarded as a useful indicator for identifying machine-generated text, our findings suggest its limitations in multi-model and multilingual contexts. Our approach ranked 6th in Subtask A, but a submission issue left our Subtask B unranked, where it would have placed 23rd.
AI-Monitors at GenAI Detection Task 1: Fast and Scalable Machine Generated Text Detection (2025)

We describe the work carried out by our team, AI-Monitors, on the Binary Multilingual Machine-Generated Text Detection (Human vs. Machine) task at COLING 2025. This task aims to determine whether a given text is generated by a machine or authored by a human. We propose a lightweight, simple, and scalable approach using encoder models such as RoBERTa and XLM-R We provide an in-depth analysis based on our experiments. Our study found that carefully exploring fine-tuned parameters such as i) no. of training epochs, ii) maximum input size, iii) handling class imbalance etc., plays an important role in building an effective system to achieve good results and can significantly impact the underlying tasks. We found the optimum setting of these parameters can lead to a difference of about 5-6{\%} in absolute terms for measure such as accuracy and F1 measure. The paper presents crucial insights into optimal parameter selection for fine-tuning RoBERTa and XLM-R based models to detect whether a given text is generated by a machine or a human.
Advacheck at GenAI Detection Task 1: AI Detection Powered by Domain-Aware Multi-Tasking (2025)

The paper describes a system designed by Advacheck team to recognise machine-generated and human-written texts in the monolingual subtask of GenAI Detection Task 1 competition. Our developed system is a multi-task architecture with shared Transformer Encoder between several classification heads. One head is responsible for binary classification between human-written and machine-generated texts, while the other heads are auxiliary multiclass classifiers for texts of different domains from particular datasets. As multiclass heads were trained to distinguish the domains presented in the data, they provide a better understanding of the samples. This approach led us to achieve the first place in the official ranking with 83.07{\%} macro F1-score on the test set and bypass the baseline by 10{\%}. We further study obtained system through ablation, error and representation analyses, finding that multi-task learning outperforms single-task mode and simultaneous tasks form a cluster structure in embeddings space.
GenAI Content Detection Task 1: English and Multilingual Machine-Generated Text Detection: AI vs. Human (2025)

We present the GenAI Content Detection Task 1 {--} a shared task on binary machine generated text detection, conducted as a part of the GenAI workshop at COLING 2025. The task consists of two subtasks: Monolingual (English) and Multilingual. The shared task attracted many participants: 36 teams made official submissions to the Monolingual subtask during the test phase and 27 teams {--} to the Multilingual. We provide a comprehensive overview of the data, a summary of the results {--} including system rankings and performance scores {--} detailed descriptions of the participating systems, and an in-depth analysis of submissions.
CIC-NLP at GenAI Detection Task 1: Advancing Multilingual Machine-Generated Text Detection (2025)

Machine-written texts are gradually becoming indistinguishable from human-generated texts, leading to the need to use sophisticated methods to detect them. Team CIC-NLP presents work in the Gen-AI Content Detection Task 1 at COLING 2025 Workshop: the focus of our work is on Subtask B of Task 1, which is the classification of text written by machines and human authors, with particular attention paid to identifying multilingual binary classification problem. Usng mBERT, we addressed the binary classification task using the dataset provided by the GenAI Detection Task team. mBERT acchieved a macro-average F1-score of 0.72 as well as an accuracy score of 0.73.
CIC-NLP at GenAI Detection Task 1: Leveraging DistilBERT for Detecting Machine-Generated Text in English (2025)

As machine-generated texts (MGT) become increasingly similar to human writing, these dis- tinctions are harder to identify. In this paper, we as the CIC-NLP team present our submission to the Gen-AI Content Detection Workshop at COLING 2025 for Task 1 Subtask A, which involves distinguishing between text generated by LLMs and text authored by humans, with an emphasis on detecting English-only MGT. We applied the DistilBERT model to this binary classification task using the dataset provided by the organizers. Fine-tuning the model effectively differentiated between the classes, resulting in a micro-average F1-score of 0.70 on the evaluation test set. We provide a detailed explanation of the fine-tuning parameters and steps involved in our analysis.
nits\_teja\_srikar at GenAI Detection Task 2: Distinguishing Human and AI-Generated Essays Using Machine Learning and Transformer Models (2025)

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.
IntegrityAI at GenAI Detection Task 2: Detecting Machine-Generated Academic Essays in English and Arabic Using ELECTRA and Stylometry (2025)

We present a robust system for detecting machine-generated academic essays, leveraging pre-trained, transformer-based models specifically tailored for both English and Arabic texts. Our primary approach utilizes ELECTRA-Small for English and AraELECTRA-Base for Arabic, fine-tuned to deliver high performance while balancing computational efficiency. By incorporating stylometric features, such as word count, sentence length, and vocabulary richness, our models excel at distinguishing between human-written and AI-generated content. Proposed models achieved excellent results with an F1- score of 99.7{\%}, ranking second among of 26 teams in the English subtask, and 98.4{\%}, finishing first out of 23 teams in the Arabic one. Main Contributions include: (1) We develop lightweight and efficient models using ELECTRA-Small and AraELECTRA-Base, achieving an impressive F1-score of 98.5{\%} on the English dataset and 98.4{\%} on the Arabic dataset. This demonstrates the power of combining transformer-based architectures with stylometric analysis. (2) We optimize our system to maintain high performance while being computationally efficient, making it suitable for deployment on GPUs with moderate memory capacity. (3) Additionally, we tested larger models, such as ELECTRA-Large, achieving an even higher F1-score of 99.7{\%} on the English dataset, highlighting the potential for further accuracy gains when using more computationally intensive models.
CMI-AIGCX at GenAI Detection Task 2: Leveraging Multilingual Proxy LLMs for Machine-Generated Text Detection in Academic Essays (2025)

This paper presents the approach we proposed for GenAI Detection Task 2, which aims to classify a given text as either machine-generated or human-written, with a particular emphasis on academic essays. We participated in subtasks A and B, which focus on detecting English and Arabic essays, respectively. We propose a simple and efficient method for detecting machine-generated essays, where we use the Llama-3.1-8B as a proxy to capture the essence of each token in the text. These essences are processed and classified using a refined feature classification network. Our approach does not require fine-tuning the LLM. Instead, we leverage its extensive multilingual knowledge acquired during pretraining to significantly enhance detection performance. The results validate the effectiveness of our approach and demonstrate that leveraging a proxy model with diverse multilingual knowledge can significantly enhance the detection of machine-generated text across multiple languages, regardless of model size. In Subtask A, we achieved an F1 score of 99.9{\%}, ranking first out of 26 teams. In Subtask B, we achieved an F1 score of 96.5{\%}, placing fourth out of 22 teams, with the same score as the third-place team.
EssayDetect at GenAI Detection Task 2: Guardians of Academic Integrity: Multilingual Detection of AI-Generated Essays (2025)

Detecting AI-generated text in the field of academia is becoming very prominent. This paper presents a solution for Task 2: AI vs. Hu- man {--} Academic Essay Authenticity Challenge in the COLING 2025 DAIGenC Workshop 1. The rise of Large Language models (LLMs) like ChatGPT has posed significant challenges to academic integrity, particularly in detecting AI-generated essays. To address this, we pro- pose a fusion model that combines pre-trained language model embeddings with stylometric and linguistic features. Our approach, tested on both English and Arabic, utilizes adaptive training and attention mechanisms to enhance F1 scores, address class imbalance, and capture linguistic nuances across languages. This work advances multilingual solutions for detecting AI-generated text in academia.
CNLP-NITS-PP at GenAI Detection Task 2: Leveraging DistilBERT and XLM-RoBERTa for Multilingual AI-Generated Text Detection (2025)

In today`s digital landscape, distinguishing between human-authored essays and content generated by advanced Large Language Models such as ChatGPT, GPT-4, Gemini, and LLaMa has become increasingly complex. This differentiation is essential across sectors like academia, cybersecurity, social media, and education, where the authenticity of written material is often crucial. Addressing this challenge, the COLING 2025 competition introduced Task 2, a binary classification task to separate AI-generated text from human-authored content. Using a benchmark dataset for English and Arabic, developing a methodology that fine-tuned various transformer-based neural networks, including CNN-LSTM, RNN, Bi-GRU, BERT, DistilBERT, GPT-2, and RoBERTa. Our Team CNLP-NITS-PP achieved competitive performance through meticulous hyperparameter optimization, reaching a Recall score of 0.825. Specifically, we ranked 18th in the English sub-task A with an accuracy of 0.77 and 20th in the Arabic sub-task B with an accuracy of 0.59. These results underscore the potential of transformer-based models in academic settings to detect AI-generated content effectively, laying a foundation for more advanced methods in essay authenticity verification.
RA at GenAI Detection Task 2: Fine-tuned Language Models For Detection of Academic Authenticity, Results and Thoughts (2025)

This paper assesses the performance of {\textquotedblleft}RA{\textquotedblright} in the Academic Essay Authenticity Challenge, which saw nearly 30 teams participating in each subtask. We employed cutting-edge transformer-based models to achieve our results. Our models consistently exceeded both the mean and median scores across the tasks. Notably, we achieved an F1-score of 0.969 in classifying AI-generated essays in English and an F1-score of 0.957 for classifying AI-generated essays in Arabic. Additionally, this paper offers insights into the current state of AI-generated models and argues that the benchmarking methods currently in use do not accurately reflect real-world scenarios.
Tesla at GenAI Detection Task 2: Fast and Scalable Method for Detection of Academic Essay Authenticity (2025)

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.
GenAI Content Detection Task 2: AI vs. Human -- Academic Essay Authenticity Challenge (2025)

This paper presents a comprehensive overview of the first edition of the Academic Essay Authenticity Challenge, organized as part of the GenAI Content Detection shared tasks collocated with COLING 2025. This challenge focuses on detecting machine-generated \textit{vs} human-authored essays for academic purposes. The task is defined as follows: \textit{{\textquotedblleft}Given an essay, identify whether it is generated by a machine or authored by a human.{\textquotedblright}} The challenge involves two languages: English and Arabic. During the evaluation phase, 25 teams submitted systems for English and 21 teams for Arabic, reflecting substantial interest in the task. Finally, five teams submitted system description papers. The majority of submissions utilized fine-tuned transformer-based models, with one team employing Large Language Models (LLMs) such as Llama 2 and Llama 3. This paper outlines the task formulation, details the dataset construction process, and explains the evaluation framework. Additionally, we present a summary of the approaches adopted by participating teams. Nearly all submitted systems outperformed the n-gram-based baseline, with the top-performing systems achieving F1 scores exceeding 0.98 for both languages, indicating significant progress in the detection of machine-generated text.
CNLP-NITS-PP at GenAI Detection Task 3: Cross-Domain Machine-Generated Text Detection Using DistilBERT Techniques (2025)

This paper presents a Cross-domain Machine-Generated Text Detection model developed for the COLING 2025 Workshop on Detecting AI-generated Content (DAIGenC). As large language models evolve, detecting machine-generated text becomes increasingly challenging, particularly in contexts like misinformation and academic integrity. While current detectors perform well on unseen data, they remain vulnerable to adversarial strategies, including paraphrasing, homoglyphs, misspellings, synonyms, whitespace manipulations, etc. We introduce a framework to address these adversarial tactics designed to bypass detection systems by adversarial training. Our team DistilBERT-NITS detector placed 7th in the Non-Adversarial Attacks category, and Adversarial-submission-3 achieved 17th in the Adversarial Attacks category.
Leidos at GenAI Detection Task 3: A Weight-Balanced Transformer Approach for AI Generated Text Detection Across Domains (2025)

Advancements in Large Language Models (LLMs) blur the distinction between human and machine-generated text (MGT), raising concerns about misinformation and academic dishonesty. Existing MGT detection methods often fail to generalize across domains and generator models. We address this by framing MGT detection as a text classification task using transformer-based models. Utilizing Distil-RoBERTa-Base, we train four classifiers (binary and multi-class, with and without class weighting) on the RAID dataset (Dugan et al., 2024). Our systems placed first to fourth in the COLING 2025 MGT Detection Challenge Task 3 (Dugan et al., 2025). Internal in-domain and zero-shot evaluations reveal that applying class weighting improves detector performance, especially with multi-class classification training. Our best model effectively generalizes to unseen domains and generators, demonstrating that transformer-based models are robust detectors of machine-generated text.