Robust AI-Generated Text Detection by Restricted Embeddings
<|reference_start|>Robust AI-Generated Text Detection by Restricted Embeddings: 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<|reference_end|>
@article{kuznetsov2024robust,
    title={Robust AI-Generated Text Detection by Restricted Embeddings},
    author={Kristian Kuznetsov, Eduard Tulchinskii, Laida Kushnareva, German
  Magai, Serguei Barannikov, Sergey Nikolenko, Irina Piontkovskaya},
    journal={arXiv preprint arXiv:2410.08113},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.08113},
    primaryClass={cs.CL cs.AI}
}