T ech E xperts( IPN ) at G en AI Detection Task 1: Detecting AI -Generated Text in E nglish and Multilingual Contexts (2025)

Abstract: The ever-increasing spread of AI-generated text, driven by the considerable progress in large language models, entails a real problem for all digital platforms: how to ensure con tent authenticity. The team TechExperts(IPN) presents a method for detecting AI-generated content in English and multilingual contexts, using the google/gemma-2b model fine-tuned for COLING 2025 shared task 1 for English and multilingual. Training results show peak F1 scores of 97.63% for English and 97.87% for multilingual detection, highlighting the model’s effectiveness in supporting content integrity across platforms.

## introduction the rise of large language models (llms), such as gpt-4, has significantly increased the volume of ai-generated content across various digital platforms. these models can generate coherent and contextually relevant text, making it much more difficult for users to distinguish between humanauthored and machine-generated content. the recent rise in ai-generated content is making many question the credibility and reliability of information, especially regarding journalism, academia, and social media, where the integrity of the content is critical. this has brought the need to develop effective methods to detect ai-generated content to an all-time high @xcite . recent gains in the capabilities of llms have brought new challenges to their detection. approaches such as reinforcement learning with human feedback and instruction tuning have given these models more versatility to follow even complex prompts and thus develop plausible responses that further complicate the detection problem @xcite . traditional detection methods, which rely on identifying patterns of word choice, sentence structure, or perplexity, are often insufficient as these models improve in mimicking hu-man writing styles @xcite . one avenue of research lies in resorting to transformer-based models in the detection effort, whereby such models make possible fine-grained differentiation of human-generated from ai-generated texts through fine-tuning curated datasets @xcite . these models have demonstrated high accuracy in identifying ai-generated content, even when the text is short or resembles typical human writing @xcite . the proposed research introduces an approach that utilizes the google/gemma-2b model, an advanced large language model (llm), to identify ai-generated content across english and multilingual contexts, using both the coling_2025_mgt_en and col-ing_2025_mgt_multilingual datasets. by leveraging google/gemma-2b, a powerful multilingual llm, our approach aims to enhance detection precision through sophisticated machine learning techniques. this method is expected to contribute significantly to maintaining content integrity and mitigating risks associated with the improper use of ai-generated textual content across diverse linguistic landscapes. this paper is structured as follows: section 2 reviews related work in ai-generated content detection, section 3 describes the methodology and dataset used, section 4 presents the experimental results, and section 5 discusses the findings and their implications. finally, we conclude with potential future directions for research in this field. ## related work detecting ai-generated content has become a critical research area due to advancements in large language models (llms) like gpt-4. these models can produce content that closely mimics human writing, raising concerns about authenticity across academia, journalism, and social media. early methods relied on lexical, syntactic, and stylistic features, but these often fell short as modern llms became more sophisticated. detecting machinegenerated text is a complex task foot_0 . recent advancements involve machine learning, particularly transformer models like roberta and bert, which show high accuracy when fine-tuned on human and machine-generated datasets. studies have demonstrated significant improvements using these models on specialized datasets @xcite . advanced approaches leverage token-level analysis, focusing on log probabilities and entropy to detect patterns typical of ai-generated text. this strategy exploits the probabilistic nature of llms, identifying subtle deviations from human writing @xcite . ensemble methods have also effectively combined models like roberta with domain-specific classifiers. techniques such as paraphrasing and backtranslation further enhance robustness, allowing better generalization across different text sources @xcite . emerging trends focus on hybrid approaches, blending linguistic features with machine learning models to capture nuances that traditional statistical methods miss. zero-shot learning methods are also being explored, enabling detection without explicit examples, though with mixed success @xcite . ethical considerations are increasingly important, particularly avoiding biases that might misclassify content from non-native english writers. future research aims to develop inclusive systems that ensure high detection accuracy across diverse user demographics @xcite . in summary, while advancements in machine learning and transformer-based models have strengthened ai-generated text detection, challenges remain, particularly in addressing diverse linguistic contexts. motivated by these backgrounds, we employ the google/gemma-2b model (a multilingual llm) to localize ai-generated text in english and multilingual with coling_2025_mgt_en and col-ing_2025_mgt_multilingual datasets, respectively. this approach aims to enhance detection accuracy, supporting efforts to uphold content integrity and responsible ai use across varied languages and settings. ## methodology this section outlines the datasets employed and the proposed google/gemma-2b model used for both english and multilingual settings. ## results and analysis this section presented the experimental setup, evaluation metrics, and training results, demonstrating the proposed model's high accuracy and robust multilingual detection performance. ## conclusion and future directions the google/gemma-2b model effortlessly detects ai-generated content and performs well even in multilingual contexts, as the context has not only been in english. this study shows that it could be used as a content authenticity tool. in the fu-ture, we are planning to expand detection not just with new models but also by increasing languages to help more communities (arabic, urdu, persian, chinese), adapting better from one ai model to another, and finally enhancing how real-time we could be so that it won't only be used for moderation content but even fact-checking.