OSINT at G en AI Detection Task 1: Multilingual MGT Detection: Leveraging Cross-Lingual Adaptation for Robust LLM s Text Identification (2025)

Abstract: 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.

## introduction large language models (llms) have been quickly adopted in mainstream, making machine-generated content readily available across various platforms, such as news, social media, q&a forums, education and academics. recent models, chatgpt, gpt-4 and llama, can deliver quality responses to diverse prompts. however, the ease with which these models can articulate human-like text has raised concerns about potential misuse and information integrity @xcite . given that humans have very slim chance of distinguishing machinegenerated text from human-written content, there is an urgent need for automated detection systems. many researches are underway developing various methods and models to address the challenge of distinguish mgt from human-authored content. most of these works focus on english text, but struggle to differentiate text in other languages effectively. while the overall accuracy is generally high, the class-wise f1 scores remain low. to tackle these issues, the coling 2025 workshop on daigenc @xcite ) "task 1: binary multilingual machine-generated text (mgt) detection (human vs. machine)" aim to refresh training and testing data with generations from novel llms and include new languages. the task is framed as-"determining whether a given text is generated by a machine or authored by a human"and is divided into two sub-tasks: subtask a: english-only mgt detection. subtask b: multilingual mgt detection. this paper focuses on subtask b. our approach for multilingual mgt detection includes cross-lingual adaptation and model generalization strategies. this methodology leverages language-specific embeddings to improve generalization across languages and models. through this our model, multi-task detection mldet aims to balanced performance on both macro and micro f1 scores. ## background over the last few years, numerous approaches have been proposed to tackle the task of machinegenerated text detection. detecting machinegenerated text is primarily formulated as a binary classification task @xcite @xcite , naively distinguishing between human-written and machinegenerated text. in general, there are three main approaches: the supervised methods @xcite @xcite @xcite , the unsupervised ones, such as zero-shot methods @xcite @xcite @xcite and adversarial measures on detection accuracy @xcite , especially within the education domain. for example, antoun et al. 2023 evaluates the robustness of detectors against characterlevel perturbations or misspelled words, focusing on french as a case study. @xcite train a generative model (dipper) to paraphrase paragraphs to evade detection. although supervised approaches yield relatively better results, they are susceptible to overfitting @xcite . there are few multilingual mgt detection techniques which are mainly based on finetuned models @xcite @xcite . ## proposed model in this section, we outline our approach for multilingual mgt detection. ## results the comprehensive analysis of the performance of various models on mgt detection based on micro f1, macro f1 score and accuracy are presented in the result of evaluating on the test dataset is tabulated in ## analysis the performance of our mldet model, as presented in tables 3 and 4 , highlights its strengths in achieving a balanced macro and micro f1 score. while direct plm (mbert) models may perform better in terms of accuracy and micro f1, their low class-wise (macro) f1 scores indicate a bias toward majority classes in the dataset (as discussed in however, the final model performs better on the development dataset compared to the test dataset. as noted in section 2, the languages and generation models in the training and development datasets are similar, whereas the test dataset introduces different languages and generation models. despite this increased challenge, the model still outperforms others in this scenario. ## conclusions in conclusion, the robust performance of mldet on diverse multilingual datasets underscores the importance of incorporating cross-lingual adaptation and model generalization strategies. a robust performance on the test dataset with a macro f1 score of 0.7067. by capturing a wide range of linguistic and contextual information, these strategies allow the model to generalize effectively across languages and domains, positioning mldet as a versatile and efficient solution for mgt detection in multilingual settings.