Introduction
In recent years there have been significant improvements in the area of large language models that are capable of generating human-like text. Several variants of such language models are designed for specific tasks such as summarization, translation, paraphrasing, etc. Recent advancements in conversational language models such as ChatGPT and GPT-4 OpenAI ( 2023 ) have demonstrated how these language models can generate incredibly human-like text, along with serving as an AI assistant for several use cases such as creative writing, explanation of ideas and concepts, code generation and correction, solving mathematical proofs etc. Bubeck et al. ( 2023 ) . However, along with improved progress in machine generation of text, there is also a growing concern about how these technologies may be misused and abused by malicious actors. Given how convincing some of these machine-generated texts are, malicious actors may use these models to propagate misinformation/disinformation Zellers et al. ( 2019 ) , propaganda Varol et al. ( 2017 ) , or even spam/scams. With the accessibility and ease of use of newer language models that have public-facing APIs, the risk of these technologies being used for generating disinformation or misleading information at scale has increased significantly De Angelis et al. ( 2023 ) and hence has prompted researchers to worry about detection and mitigation strategies Zhou et al. ( 2023 ) .

For example, recently, there have been concerns about misleading news websites hosting fully AI-generated news articles 1 1 1 https://www.newsguardtech.com/special-reports/newsbots-ai-generated-news-websites-proliferating/ . Such unprecedented improvement in language generation capabilities hence naturally necessitates the development of detectors that can accurately and reliably classify such generated text. Motivated by this, we focus on the sub-problem of AI-generated news detection.

A major issue surrounding building a supervised classifier for AI-generated text is the sheer variety of large language models that are available for use. Prior work Jawahar et al. ( 2020 ) has demonstrated that detectors built to identify text generated by a particular generator struggle with text from other generators. Furthermore, for newer generators, it might even be impossible to collect and curate labeled training datasets, since access to such models might be limited or even forbidden. Given
this data problem, in this paper, we consider the situation where we have access to text from a generator but we do not know which generator it came from. However, we do have labeled data from some generators. In this context, we propose a framework for AI-generated text detection that can perform well on target data in the absence of labels. We frame this problem as an unsupervised domain adaptation problem, assuming we have labeled data from a source generator and unlabeled data from (perhaps newer) target generators.
Our framework also uses a contrastive loss component that acts as a regularizer and helps the model learn invariant features and avoid overfitting to the particular generator it was trained on, hence improving performance on the unknown generator (Figure 1 ). For news text, our model achieves performance with a 0.8\% margin of a fully supervised detector. Our main contributions in this paper are:

We propose a novel AI-generated text detection framework, ConDA , that uses unsupervised domain adaptation and self-supervised contrastive learning to effectively leverage labeled source domain and unlabeled target domain data.

Through extensive evaluations on benchmark human/AI-generated news datasets, spanning a variety of LLMs, we show that ConDA effectively solves the problem of label scarcity, and achieves state-of-the-art performance for unsupervised detection.

Furthermore, we create our own ChatGPT-generated data and via a case study, show the efficacy of our model on text generated using new conversational language models.