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Feature-Level Insights into Artificial Text Detection with Sparse Autoencoders

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arXiv 2025年

作者： Kuznetsov, Kristian Kushnareva, Laida Druzhinina, Polina Razzhigaev, Anton Voznyuk, Anastasia Piontkovskaya, Irina Burnaev, Evgeny Barannikov, Serguei Skolkovo Institute of Science and Technology Russia AI Foundation and Algorithm Lab United States Moscow Institute of Physics and Technology Russia CNRS Université Paris Cité France Russia

Artificial Text Detection (ATD) is becoming increasingly important with the rise of advanced Large Language Models (LLMs). Despite numerous efforts, no single algorithm performs consistently well across different types of unseen text or guarantees effective generalization to new LLMs. Interpretability plays a crucial role in achieving this goal. In this study, we enhance ATD interpretability by using Sparse Autoencoders (SAE) to extract features from Gemma-2-2b’s residual stream. We identify both interpretable and efficient features, analyzing their semantics and relevance through domain- and model-specific statistics, a steering approach, and manual or LLM-based interpretation. Our methods offer valuable insights into how texts from various models differ from human-written content. We show that modern LLMs have a distinct writing style, especially in information-dense domains, even though they can produce human-like outputs with personalized prompts. © 2025, CC BY.

关键词： Semantics

One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation

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arXiv 2025年

作者： Selikhanovych, Daniil Li, David Leonov, Aleksei Gushchin, Nikita Kushneriuk, Sergei Filippov, Alexander Burnaev, Evgeny Koshelev, Iaroslav Korotin, Alexander HSE University Russia Yandex Research AI Foundation Algorithm Lab Skolkovo Institute of Science and Technology Russia Moscow Institute of Physics and Technology Russia Artificial Intelligence Research Institute

Diffusion models for super-resolution (SR) produce high-quality visual results but require expensive computational costs. Despite the development of several methods to accelerate diffusion-based SR models, some (e.g., SinSR) fail to produce realistic perceptual details, while others (e.g., OSEDiff) may hallucinate non-existent structures. To overcome these issues, we present RSD, a new distillation method for ResShift, one of the top diffusion-based SR models. Our method is based on training the student network to produce such images that a new fake ResShift model trained on them will coincide with the teacher model. RSD achieves single-step restoration and outperforms the teacher by a large margin. We show that our distillation method can surpass the other distillation-based method for ResShift - SinSR - making it on par with state-of-the-art diffusion-based SR distillation methods. Compared to SR methods based on pre-trained text-to-image models, RSD produces competitive perceptual quality, provides images with better alignment to degraded input images, and requires fewer parameters and GPU memory. We provide experimental results on various real-world and synthetic datasets, including RealSR, RealSet65, DRealSR, ImageNet, and DIV2K. Copyright © 2025, The Authors. All rights reserved.

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Scalar Function Topology Divergence: Comparing Topology of 3D Objects

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arXiv 2024年

作者： Trofimov, Ilya Voronkova, Daria Tulchinskii, Eduard Burnaev, Evgeny Barannikov, Serguei Skolkovo Institute of Science and Technology Moscow Russia AIRI Moscow Russia CNRS IMJ Paris Cité University France AI Foundation and Algorithm Lab Moscow Russia

We propose a new topological tool for computer vision - Scalar Function Topology Divergence (SFTD), which measures the dissimilarity of multi-scale topology between sublevel sets of two functions having a common domain. Functions can be defined on an undirected graph or Euclidean space of any dimensionality. Most of the existing methods for comparing topology are based on Wasserstein distance between persistence barcodes and they don't take into account the localization of topological features. The minimization of SFTD ensures that the corresponding topological features of scalar functions are located in the same places. The proposed tool provides useful visualizations depicting areas where functions have topological dissimilarities. We provide applications of the proposed method to 3D computer vision. In particular, experiments demonstrate that SFTD as an additional loss improves the reconstruction of cellular 3D shapes from 2D fluorescence microscopy images, and helps to identify topological errors in 3D segmentation. Additionally, we show that SFTD outperforms Betti matching loss in 2D segmentation problems. The code is publicly available: https://***/IlyaTrofimov/SFTD. © 2024, CC BY.

关键词： Fluorescence microscopy

Intrinsic Dimension Estimation for Robust Detection of ai-Generated Texts

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arXiv 2023年

作者： Tulchinskii, Eduard Kuznetsov, Kristian Kushnareva, Laida Cherniavskii, Daniil Nikolenko, Sergey Burnaev, Evgeny Barannikov, Serguei Piontkovskaya, Irina Skolkovo Institute of Science and Technology Russia AI Foundation and Algorithm Lab Russia Russia CNRS Université Paris Cité France St. Petersburg Department the Steklov Institute of Mathematics Russia

Rapidly increasing quality of ai-generated content makes it difficult to distinguish between human and ai-generated texts, which may lead to undesirable consequences for society. Therefore, it becomes increasingly important to study the properties of human texts that are invariant over different text domains and varying proficiency of human writers, can be easily calculated for any language, and can robustly separate natural and ai-generated texts regardless of the generation model and sampling method. In this work, we propose such an invariant for human-written texts, namely the intrinsic dimensionality of the manifold underlying the set of embeddings for a given text sample. We show that the average intrinsic dimensionality of fluent texts in a natural language is hovering around the value 9 for several alphabet-based languages and around 7 for Chinese, while the average intrinsic dimensionality of ai-generated texts for each language is ≈ 1.5 lower, with a clear statistical separation between human-generated and ai-generated distributions. This property allows us to build a score-based artificial text detector. The proposed detector’s accuracy is stable over text domains, generator models, and human writer proficiency levels, outperforming SOTA detectors in model-agnostic and cross-domain scenarios by a significant margin. We release code and dataMSC Codes 68T50 © 2023, CC BY.

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Robust ai-Generated Text Detection by Restricted Embeddings

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arXiv 2024年

作者： Kuznetsov, Kristian Tulchinskii, Eduard Kushnareva, Laida Magai, German Barannikov, Serguei Nikolenko, Sergey Piontkovskaya, Irina AI Foundation and Algorithm Lab Russia HSE University Russia Noeon Research Japan Skolkovo Institute of Science and Technology Russia CNRS Université Paris Cité France ISP RAS Research Center for Trusted Artificial Intelligence Moscow Russia St. Petersburg Department of the Steklov Institute of Mathematics Russia

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. © 2024, CC BY.

关键词： Embeddings

ai-generated text boundary detection with RoFT

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arXiv 2023年

作者： Kushnareva, Laida Gaintseva, Tatiana Magai, German Barannikov, Serguei Abulkhanov, Dmitry Kuznetsov, Kristian Tulchinskii, Eduard Piontkovskaya, Irina Nikolenko, Sergey AI Foundation and Algorithm Lab Russia Digital Environment Research Institute Queen Mary University of London United Kingdom HSE University Russia Noeon Research Japan Skolkovo Institute of Science and Technology Russia CNRS Université Paris Cité France St. Petersburg Department The Steklov Institute of Mathematics Russia

Due to the rapid development of large language models, people increasingly often encounter texts that may start as written by a human but continue as machine-generated. Detecting the boundary between human-written and machine-generated parts of such texts is a challenging problem that has not received much attention in literature. We attempt to bridge this gap and examine several ways to adapt state of the art artificial text detection classifiers to the boundary detection setting. We push all detectors to their limits, using the Real or Fake text benchmark that contains short texts on several topics and includes generations of various language models. We use this diversity to deeply examine the robustness of all detectors in cross-domain and cross-model settings to provide baselines and insights for future research. In particular, we find that perplexity-based approaches to boundary detection tend to be more robust to peculiarities of domain-specific data than supervised fine-tuning of the RoBERTa model;we also find which features of the text confuse boundary detection algorithms and negatively influence their performance in cross-domain settings. © 2023, CC BY.

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Improving Interpretability and Robustness for the Detection of ai-Generated Images

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arXiv 2024年

作者： Gaintseva, Tatiana Kushnareva, Laida Magai, German Piontkovskaya, Irina Nikolenko, Sergey Benning, Marting Barannikov, Serguei Slabaugh, Gregory Digital Environment Research Institute Queen Mary University of London United Kingdom AI Foundation and Algorithm Lab Russia HSE University Russia Noeon Research Japan Skolkovo Institute of Science and Technology Russia CNRS Universite Paris Cite France St. Petersburg Department Steklov Institute of Mathematics Russia Department of Computer Science University College London United Kingdom

With growing abilities of generative models, artificial content detection becomes an increasingly important and difficult task. However, all popular approaches to this prcoblem suffer from poor generalization across domains and generative models. In this work, we focus on the robustness of ai-generated image (aiGI) detectors. We analyze existing state-of-the-art aiGI detection methods based on frozen CLIP embeddings and show how to interpret them, shedding light on how images produced by various ai generators differ from real ones. Next we propose two ways to improve robustness: based on removing harmful components of the embedding vector and based on selecting the best performing attention heads in the image encoder model. Our methods increase the mean out-of-distribution (OOD) classification score by up to 6% for cross-model transfer. We also propose a new dataset for aiGI detection and use it in our evaluation;we believe this dataset will help boost further research. The dataset and code are provided as a supplement. © 2024, CC BY.

关键词： Embeddings

Intrinsic dimension estimation for robust detection of ai-generated texts 23

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Intrinsic dimension estimation for robust detection of AI-ge...

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Eduard Tulchinskii Kristian Kuznetsov Laida Kushnareva Daniil Cherniavskii Sergey Nikolenko Evgeny Burnaev Serguei Barannikov Irina Piontkovskaya Skolkovo Institute of Science and Technology Russia AI Foundation and Algorithm Lab Russia Artificial Intelligence Research Institute (AIRI) Russia St. Petersburg Department of the Steklov Institute of Mathematics Russia Skolkovo Institute of Science and Technology Russia and Artificial Intelligence Research Institute (AIRI) Russia Skolkovo Institute of Science and Technology Russia and CNRS Université Paris Cité France

Rapidly increasing quality of ai-generated content makes it difficult to distinguish between human and ai-generated texts, which may lead to undesirable consequences for society. Therefore, it becomes increasingly important to study the properties of human texts that are invariant over different text domains and varying proficiency of human writers, can be easily calculated for any language, and can robustly separate natural and ai-generated texts regardless of the generation model and sampling method. In this work, we propose such an invariant for human-written texts, namely the intrinsic dimensionality of the manifold underlying the set of embeddings for a given text sample. We show that the average intrinsic dimensionality of fluent texts in a natural language is hovering around the value 9 for several alphabet-based languages and around 7 for Chinese, while the average intrinsic dimensionality of ai-generated texts for each language is ≈ 1.5 lower, with a clear statistical separation between human-generated and ai-generated distributions. This property allows us to build a score-based artificial text detector. The proposed detector's accuracy is stable over text domains, generator models, and human writer proficiency levels, outperforming SOTA detectors in model-agnostic and cross-domain scenarios by a significant margin. We release code and data ***/ArGintum/GPTID.

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