区分人类、AI生成与人机协作文本，还能识别背后的AI模型家族。
FAID: Fine-Grained AI-Generated Text Detection Using Multi-Task Auxiliary and Multi-Level Contrastive Learning
- 多任务辅助+多层次对比学习，捕捉细微风格特征。
- 在未见领域和新模型上准确率显著提升，泛化能力强。
- 适合需要透明写作与责任追溯的场景，如学术与媒体。
人类与AI模型在生成任务中的协作日益普遍，带来了区分人类撰写、大语言模型生成及人机协作文本的新挑战。本文构建了多语言、多领域、多生成器的数据集FAIDSet，提出细粒度检测框架FAID，可将文本分类为三类，并识别其背后的LLM家族。不同于现有二元分类器，FAID兼顾作者身份与模型特异性特征。通过多层级对比学习与多任务辅助分类，模型能捕捉细微风格线索；将不同LLM家族视为独立风格实体，引入适配机制以应对分布偏移，无需重新训练即可处理未见数据。实验表明，FAID在未见领域和新型大模型上的泛化性能优于多个基线，为提升人机协作写作的透明性与问责性提供了可行方案。数据与代码已公开于https://github.com/mbzuai-nlp/FAID。
原文摘要 · Abstract (English)
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. Our data and code are available at https://github.com/mbzuai-nlp/FAID
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