Papers by Laida Kushnareva
Robust AI-Generated Text Detection by Restricted Embeddings (2024.findings-emnlp)
Copied to clipboard
Kristian Kuznetsov, Eduard Tulchinskii, Laida Kushnareva, German Magai, Serguei Barannikov, Sergey Nikolenko, Irina Piontkovskaya
| Challenge: | Existing approaches for artificial text detection are score-based and classifier-based . however, score-driven methods often rely on a score-derived score. |
| Approach: | They investigate the ability of classifier-based detectors to transfer to unseen generators or semantic domains. |
| Outcome: | The proposed methods improve the out-of-distribution classification score by up to 9% and 14%. |
Acceptability Judgements via Examining the Topology of Attention Maps (2022.findings-emnlp)
Copied to clipboard
Daniil Cherniavskii, Eduard Tulchinskii, Vladislav Mikhailov, Irina Proskurina, Laida Kushnareva, Ekaterina Artemova, Serguei Barannikov, Irina Piontkovskaya, Dmitri Piontkovski, Evgeny Burnaev
| Challenge: | Acceptability judgments are a key component of generative linguistics, but their ability to judge grammatical acceptability has not been explored. |
| Approach: | They propose to exploit the geometric properties of the attention graph to evaluate the grammatical acceptability of sentences using topological data analysis. |
| Outcome: | The proposed approach outperforms nine statistical and Transformer LM baselines on the BLiMP benchmark and the human-level performance on the same benchmark. |
Unveiling Intrinsic Dimension of Texts: from Academic Abstract to Creative Story (2026.eacl-long)
Copied to clipboard
Pedashenko Vladislav, Laida Kushnareva, Yana Khassan Nibal, Eduard Tulchinskii, Kristian Kuznetsov, Vladislav Zharchinskii, Yury Maximov, Irina Piontkovskaya
| Challenge: | a new study grounding intrinsic dimension in interpretable text properties is published . entropy-like measures are ubiquitous in training and evaluation, but geometric complexity remains underexplored. |
| Approach: | They propose to ground intrinsic dimension (ID) in interpretable text properties through cross-encoder analysis, linguistic features, and sparse autoencodes. |
| Outcome: | The proposed method shows that scientific prose shows low ID ( 8), encyclopedic content medium ID ( > 9), creative/opinion writing high ID (> 10.5) |
Artificial Text Detection via Examining the Topology of Attention Maps (2021.emnlp-main)
Copied to clipboard
Laida Kushnareva, Daniil Cherniavskii, Vladislav Mikhailov, Ekaterina Artemova, Serguei Barannikov, Alexander Bernstein, Irina Piontkovskaya, Dmitri Piontkovski, Evgeny Burnaev
| Challenge: | Existing methods for text detection lack interpretability and robustness towards unseen models. |
| Approach: | They propose three new types of interpretable topological features based on topological data analysis which is currently understudied in the field of NLP. |
| Outcome: | The proposed features outperform count- and neural-based baselines up to 10% on three common datasets and tend to be the most robust towards unseen GPT-style generation models. |
Feature-Level Insights into Artificial Text Detection with Sparse Autoencoders (2025.findings-acl)
Copied to clipboard
Kristian Kuznetsov, Laida Kushnareva, Anton Razzhigaev, Polina Druzhinina, Anastasia Voznyuk, Irina Piontkovskaya, Evgeny Burnaev, Serguei Barannikov
| Challenge: | Existing algorithms for AI text detection lack interpretability, limiting their reliability in highstakes applications. |
| Approach: | They extend existing ATD frameworks by using Sparse Autoencoders to extract features from Gemma-2-2b residual stream. |
| Outcome: | The proposed algorithms can extract human-interpretable features from Gemma-2-2b model. |
AudioSAE: Towards Understanding of Audio-Processing Models with Sparse AutoEncoders (2026.eacl-long)
Copied to clipboard
Georgii Aparin, Tasnima Sadekova, Alexey Rukhovich, Assel Yermekova, Laida Kushnareva, Vadim Popov, Kristian Kuznetsov, Irina Piontkovskaya
| Challenge: | Feature steering reduces Whisper’s false speech detections by 70% with negligible WER increase, demonstrating real-world applicability. |
| Approach: | They train Sparse Autoencoders across all encoder layers of Whisper and HuBERT and evaluate their stability, interpretability, and practical utility. |
| Outcome: | The proposed models capture general acoustic and semantic information as well as specific events, including environmental noises and paralinguistic sounds, and disentangle them effectively. |
Quantifying Logical Consistency in Transformers via Query-Key Alignment (2025.emnlp-main)
Copied to clipboard
Eduard Tulchinskii, Laida Kushnareva, Anastasia Voznyuk, Andrei Andriiainen, Irina Piontkovskaya, Evgeny Burnaev, Serguei Barannikov
| Challenge: | Existing solutions for multi-step logical reasoning are unreliable . Existing methods generate intermediate steps but provide no internal check of coherence . |
| Approach: | They propose a method that uses internal Query-Key interactions within transformer attention heads as a proxy for logical consistency. |
| Outcome: | The proposed method reveals latent reasoning structure in large language models and provides a mechanistic alternative to ablation-based analysis. |