Papers by Eduard Tulchinskii
Robust AI-Generated Text Detection by Restricted Embeddings (2024.findings-emnlp)
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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)
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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)
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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) |
Quantifying Logical Consistency in Transformers via Query-Key Alignment (2025.emnlp-main)
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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. |