Team U nibuc - NLP at G en AI Detection Task 1: Qwen it detect machine-generated text? (2025)

Abstract: We explored both masked language models and causal models. For Subtask A, our best model achieved first-place out of 36 teams when looking at F1 Micro (Auxiliary Score) of 0.8333, and second-place when looking at F1 Macro (Main Score) of 0.8301. For causal models, our best model was a fine-tuned version of Qwen and for masked models, our best model was a fine-tuned version of XLM-Roberta-Base.

## introduction task 1 from the genai content detection workshop @xcite focuses on discerning whether a text sample is machine-generated or human-authored. with human ability to distinguish ai-generated text from human content near random chance, advanced automated systems are needed to ensure information integrity. such systems are crucial for verifying content sources and countering unethical ai use, including propaganda, misinformation, deepfakes, and social manipulation, which pose significant societal risks. the system developed for task 1 subtask a is based on an llm model where only the last layer and the classification head were trained for the downstream task, on the other hand, the system developed for subtask b is based on a transformer model with a classification head on top and it was completely fine-tuned using low-rank adaptation (lora) @xcite . we made our models publicly available in a github repository. the competition had 3 tasks: ## system overview in this paper, we focused our research on two different system architectures: causal models (3.1) for subtask 1 and masked models (3.2) for subtask 2. ## results we participated in task 1 and achieved the top position in the monolingual subtask based on f1 micro score, as shown in in the multilingual track, however, our model didn't do so well, securing 24th place with f1 scores of 0.66 (macro) and 0.67 (micro). this gap between monolingual and multilingual performance highlights the difficulties our model encountered when adapting to varied languages and possibly diverse linguistic structures in the multilingual setting. ## conclusions and future work in conclusion, our architecture and training methods produced good results for subtask a (securing the second place). however, our models demonstrated signs of over-fitting for subtask b. our future endeavors will explore several avenues: