Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text
This addresses the societal challenge of distinguishing human from AI-generated text, which is increasingly pervasive and difficult to detect, though it is incremental as it builds on existing contrastive learning approaches.
The paper tackles the problem of detecting and attributing AI-generated text by proposing WhosAI, a contrastive learning framework that achieves outstanding results on the TuringBench benchmark, outperforming all existing methods in both detection and attribution tasks.
The significant progress in the development of Large Language Models has contributed to blurring the distinction between human and AI-generated text. The increasing pervasiveness of AI-generated text and the difficulty in detecting it poses new challenges for our society. In this paper, we tackle the problem of detecting and attributing AI-generated text by proposing WhosAI, a triplet-network contrastive learning framework designed to predict whether a given input text has been generated by humans or AI and to unveil the authorship of the text. Unlike most existing approaches, our proposed framework is conceived to learn semantic similarity representations from multiple generators at once, thus equally handling both detection and attribution tasks. Furthermore, WhosAI is model-agnostic and scalable to the release of new AI text-generation models by incorporating their generated instances into the embedding space learned by our framework. Experimental results on the TuringBench benchmark of 200K news articles show that our proposed framework achieves outstanding results in both the Turing Test and Authorship Attribution tasks, outperforming all the methods listed in the TuringBench benchmark leaderboards.