Fraunhofer SIT at G en AI Detection Task 1: Adapter Fusion for AI -generated Text Detection (2025)

Abstract: 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.

## introduction the increased utilisation of tools such as chatgpt has resulted in a notable rise in the prevalence of ai-generated text across all facets of modern life. in light of this, the development of detectors of aigenerated content is becoming increasingly important. the majority of research in this field focuses on the detection of ai-generated text in the english language. however, it is important to note that generation models are also capable of producing text in a multitude of languages. consequently, the development of effective detectors that can perform well in multilingual settings is a crucial area of investigation. the binary english and multilingual machine-generated text detection challenge at the genai content detection workshop @xcite is focusing on this problem by encouraging its participants to develop detectors of ai-generated text on english (subtask a) and multilingual (subtask b) text. we participated in both subtasks of this challenge and trained and tested different transformers and adapters on the task of ai-generated text detection. we tested different adapter configurations and the utilisation of adapter fusion to transfer knowledge of pre-trained task adapters on ai-generated text detection. ## related work several detectors of ai-generated text have been developed recently. for example, @xcite developed a system, llm-detectaive, which is capable of identifying not only text written by humans and machines, but also instances where the fact that a text was generated by a machine has been obfuscated, or cases where an llm has been employed to enhance a text that was originally written by a human. @xcite tested and trained different transformers on the detection of ai-generated text in the field of education. the transformers tested were albert, bert, elec-tra, roberta and xlnet. the results demonstrated that, without and with fine-tuning, bert provided the best results, with superior results when fine-tuning. other detectors were developed in challenges, such as the semeval-2024 task 8 challenge @xcite or the pan challenge at clef 2024 @xcite . as far as we know, adapters and adapter fusion haven't been used before. ## system description we participated in subtask a and b of the genai content detection task 1 challenge. in both settings, first, we tested different transformers, finetuning them on the respective training sets. in subtask a, the english setting, we fine-tuned the transformers bert (base and large; @xcite and roberta base @xcite . in subtask b, using the multilingual training set, we fine-tuned xlm-roberta base and large @xcite . subsequently, we trained our own task adapter on ai-generated text detection using the respective datasets of the subtasks and performed adapter fusion @xcite with official available pre-trained task adapters from adapter hub 1 @xcite . adapter fusion is a method of combining the knowledge of multiple pre-trained adapters trained for different tasks. ## evaluation results in the development phase of the challenge, the various architectural options were evaluated on the devtest set. the results of the two subtasks are presented in the following sections. subsequently, during the final test phase, the two architectures that demonstrated optimal performance on the devtest set, were tested again. ## conclusion and future work in this paper, we presented the solutions developed by our team fraunhofer sit for the 2024 genai detection task 1 challenge. we fine-tuned trans-