Robust AI-Generated Text Detection by Restricted Embeddings

• URL: http://arxiv.org/abs/2410.08113v1
• Date: Thu, 10 Oct 2024 16:58:42 GMT
• Title: Robust AI-Generated Text Detection by Restricted Embeddings
• Authors: Kristian Kuznetsov, Eduard Tulchinskii, Laida Kushnareva, German Magai, Serguei Barannikov, Sergey Nikolenko, Irina Piontkovskaya,
• Abstract summary: We focus on robustness of detectors of AI-generated text, namely their ability to transfer to unseen generators or semantic domains. We show that clearing out harmful linear subspaces helps to train a robust classifier, ignoring domain-specific spurious features. Our best approaches for head-wise and coordinate-based subspace removal increase the mean out-of-distribution (OOD) classification score by up to 9% and 14% in particular.
• Score: 6.745955674138081
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Growing amount and quality of AI-generated texts makes detecting such content more difficult. In most real-world scenarios, the domain (style and topic) of generated data and the generator model are not known in advance. In this work, we focus on the robustness of classifier-based detectors of AI-generated text, namely their ability to transfer to unseen generators or semantic domains. We investigate the geometry of the embedding space of Transformer-based text encoders and show that clearing out harmful linear subspaces helps to train a robust classifier, ignoring domain-specific spurious features. We investigate several subspace decomposition and feature selection strategies and achieve significant improvements over state of the art methods in cross-domain and cross-generator transfer. Our best approaches for head-wise and coordinate-based subspace removal increase the mean out-of-distribution (OOD) classification score by up to 9% and 14% in particular setups for RoBERTa and BERT embeddings respectively. We release our code and data: https://github.com/SilverSolver/RobustATD

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