Grape at GenAI Detection Task 1: Leveraging Compact Models and Linguistic Features for Robust Machine-Generated Text Detection
Doan, Nhi Hoai Inui, Kentaro
Doan, Nhi Hoai
Inui, Kentaro
Author
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Workshop
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
In this project, we aim to address two subtasks of Task 1: Binary Multilingual Machine-Generated Text (MGT) Detection (Human vs. Machine) as part of the COLING 2025 Workshop on MGT Detection (Wang et al., 2025) using different approaches. The first method involves separately fine-tuning small language models tailored to the specific subtask. The second approach builds on this methodology by incorporating linguistic, syntactic, and semantic features, leveraging ensemble learning to integrate these features with model predictions for more robust classification. By evaluating and comparing these approaches, we aim to identify the most effective techniques for detecting machine-generated content across languages, providing insights into improving automated verification tools amidst the rapid growth of LLM-generated text in digital spaces.
Citation
N. H. Doan and K. Inui, “Grape at GenAI Detection Task 1: Leveraging Compact Models and Linguistic Features for Robust Machine-Generated Text Detection,” 2025. Accessed: Mar. 12, 2025. [Online]. Available: https://aclanthology.org/2025.genaidetect-1.22/
Source
Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect) 2025
Conference
Keywords
Machine-generated text detection, Compact language models, Linguistic features, Ensemble learning, Multilingual analysis
Subjects
Source
Publisher
Association for Computational Linguistics