FAID: Fine-grained AI-generated Text Detection using Multi-task Auxiliary and Multi-level Contrastive Learning

• URL: http://arxiv.org/abs/2505.14271v1
• Date: Tue, 20 May 2025 12:23:31 GMT
• Title: FAID: Fine-grained AI-generated Text Detection using Multi-task Auxiliary and Multi-level Contrastive Learning
• Authors: Minh Ngoc Ta, Dong Cao Van, Duc-Anh Hoang, Minh Le-Anh, Truong Nguyen, My Anh Tran Nguyen, Yuxia Wang, Preslav Nakov, Sang Dinh,
• Abstract summary: We introduce a fine-grained detection framework FAID to classify text into three categories.<n>By modeling AI families as distinct stylistic entities, FAID offers improved interpretability.<n>It provide a potential solution for improving transparency and accountability in AI-assisted writing.
• Score: 25.243693149282965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing collaboration between humans and AI models in generative tasks has introduced new challenges in distinguishing between human-written, AI-generated, and human-AI collaborative texts. In this work, we collect a multilingual, multi-domain, multi-generator dataset FAIDSet. We further introduce a fine-grained detection framework FAID to classify text into these three categories, meanwhile identifying the underlying AI model family. Unlike existing binary classifiers, FAID is built to capture both authorship and model-specific characteristics. Our method combines multi-level contrastive learning with multi-task auxiliary classification to learn subtle stylistic cues. By modeling AI families as distinct stylistic entities, FAID offers improved interpretability. We incorporate an adaptation to address distributional shifts without retraining for unseen data. Experimental results demonstrate that FAID outperforms several baseline approaches, particularly enhancing the generalization accuracy on unseen domains and new AI models. It provide a potential solution for improving transparency and accountability in AI-assisted writing.

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