Potential harms of Large Language Models such as mass misinformation and plagiarism can be partially mitigated if there exists a reliable way to detect machine generated text. In this paper, we propose a new watermarking method to detect machine-generated texts. Our method embeds a unique pattern within the generated text, ensuring that while the content remains coherent and natural to human readers, it carries distinct markers that can be identified algorithmically. Specifically, we intervene with the token sampling process in a way which enables us to trace back our token choices during the detection phase. We show how watermarking affects textual quality and compare our proposed method with a state-of-the-art watermarking method in terms of robustness and detectability. Through extensive experiments, we demonstrate the effectiveness of our watermarking scheme in distinguishing between watermarked and non-watermarked text, achieving high detection rates while maintaining textual quality.

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.

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 vs human-authored essays for academic purposes. The task is defined as follows: “Given an essay, identify whether it is generated by a machine or authored by a human.” 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.