FAID：利用多任务辅助和多级对比学习进行细粒度AI生成文本检测
FAID: Fine-Grained AI-Generated Text Detection Using Multi-Task Auxiliary and Multi-Level Contrastive Learning
AI总结：
FAID通过多任务辅助和多级对比学习，实现对AI生成文本的细粒度分类与识别，提升AI辅助写作的透明度和可问责性。
摘要：
人类与AI模型在生成任务中的日益紧密合作，带来了区分人类撰写的文本、大语言模型生成的文本以及人类与大语言模型协作文本的新挑战。在本工作中，我们收集了一个多语言、多领域、多生成器的数据集FAIDSet。我们进一步引入了一个细粒度检测框架FAID，用于将文本分类为这三类，并识别生成器的底层大语言模型家族。与现有的二元分类器不同，FAID旨在捕捉作者身份和模型特定的特征。我们的方法结合了多级对比学习与多任务辅助分类，以学习细微的风格线索。通过将大语言模型家族视为不同的风格实体，我们引入了适应性以应对分布偏移，而无需重新训练未见过的数据。我们的实验结果表明，FAID在多个基线中表现更优，特别是在未见领域和新大语言模型上的泛化准确性得到增强，从而为提高AI辅助写作的透明性和问责制提供了潜在的解决方案。我们的数据和代码可在https://github.com/mbzuai-nlp/FAID上获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
