31 citations · 38 across the 9 of their papers we have counts for
6 papers · 1 filter
Unveiling Intrinsic Dimension of Texts: from Academic Abstract to Creative Story
Vladislav Pedashenko, Laida Kushnareva, Yana Khassan Nibal +5
Intrinsic dimension (ID) is an important tool in modern LLM analysis, informing studies of training dynamics, scaling behavior, and dataset structure, yet its textual determinants…
Feature-Level Insights into Artificial Text Detection with Sparse Autoencoders
Kristian Kuznetsov, Laida Kushnareva, Polina Druzhinina +5
Artificial Text Detection (ATD) is becoming increasingly important with the rise of advanced Large Language Models (LLMs). Despite numerous efforts, no single algorithm performs co…
Robust AI-Generated Text Detection by Restricted Embeddings
Kristian Kuznetsov, Eduard Tulchinskii, Laida Kushnareva +4
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 g…
Listening to the Wise Few: Query-Key Alignment Unlocks Latent Correct Answers in Large Language Models
Eduard Tulchinskii, Laida Kushnareva, Kristian Kuznetsov +5
Large language models (LLMs) routinely fail to output the correct option in multiple-choice question answering (MCQA) while encoding the answer internally. We expose this latent kn…
AI-generated text boundary detection with RoFT
Laida Kushnareva, Tatiana Gaintseva, German Magai +6
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 t…
Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts
Eduard Tulchinskii, Kristian Kuznetsov, Laida Kushnareva +5
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. T…