Kristian Kuznetsov
2024
Robust AI-Generated Text Detection by Restricted Embeddings
Kristian Kuznetsov
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Eduard Tulchinskii
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Laida Kushnareva
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German Magai
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Serguei Barannikov
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Sergey Nikolenko
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Irina Piontkovskaya
Findings of the Association for Computational Linguistics: EMNLP 2024
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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Co-authors
- Eduard Tulchinskii 1
- Laida Kushnareva 1
- German Magai 1
- Serguei Barannikov 1
- Sergey Nikolenko 1
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