Large language models (LLMs) are increasingly being used for generating text in a variety of use cases, including journalistic news articles. Given the potential malicious nature in which these LLMs can be used to generate disinformation at scale, it is important to build effective detectors for such AI-generated text. Given the surge in development of new LLMs, acquiring labeled training data for supervised detectors is a bottleneck. However, there might be plenty of unlabeled text data available, without information on which generator it came from. In this work we tackle this data problem, in detecting AI-generated news text, and frame the problem as an unsupervised domain adaptation task. Here the domains are the different text generators, i.e. LLMs, and we assume we have access to only the labeled source data and unlabeled target data. We develop a Contrastive Domain Adaptation framework, called ConDA, that blends standard domain adaptation techniques with the representation power of contrastive learning to learn domain invariant representations that are effective for the final unsupervised detection task. Our experiments demonstrate the effectiveness of our framework, resulting in average performance gains of 31.7% from the best performing baselines, and within 0.8% margin of a fully supervised detector. All our code and data is available at https://github.com/AmritaBh/ConDA-gen-text-detection.

翻译：大语言模型（LLMs）正越来越多地被用于多种场景下的文本生成，包括新闻类文章。鉴于这些LLMs可能被恶意用于大规模制造虚假信息，构建有效的AI生成文本检测器至关重要。随着新LLMs的迅猛发展，为有监督检测器获取标注训练数据成为瓶颈。然而，可能存在大量无标注的文本数据，且缺乏其生成来源信息。本文针对AI生成新闻文本检测中的这一数据问题，将其构建为无监督领域自适应任务。其中，不同文本生成器（即LLMs）构成不同领域，我们假设仅能访问有标注源域数据和无标注目标域数据。我们提出一种名为ConDA的对比领域自适应框架，该框架将标准领域自适应技术与对比学习的表征能力相结合，以学习对最终无监督检测任务有效的领域不变表征。实验结果表明，我们的框架显著有效，相比最优基线方法平均性能提升31.7%，且与全监督检测器的性能差距仅在0.8%以内。所有代码与数据已开源：https://github.com/AmritaBh/ConDA-gen-text-detection。