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📄 Abstract
Abstract: The growing collaboration between humans and AI models in generative tasks
has introduced new challenges in distinguishing between human-written,
LLM-generated, and human--LLM 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, and also to identify the underlying LLM family of the
generator. 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 LLM families as distinct stylistic entities, we
incorporate an adaptation to address distributional shifts without retraining
for unseen data. Our experimental results demonstrate that FAID outperforms
several baselines, particularly enhancing the generalization accuracy on unseen
domains and new LLMs, thus offering a potential solution for improving
transparency and accountability in AI-assisted writing.
Key Contributions
FAID is a fine-grained AI-generated text detection framework that classifies text into human-written, LLM-generated, or collaborative categories, and identifies the LLM family. It uses multi-level contrastive learning and multi-task auxiliary classification to capture subtle stylistic cues and incorporates adaptation for distributional shifts.
Business Value
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