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Eduard Tulchinskii

7 accepted papers

2025

Quantifying Logical Consistency in Transformers via Query-Key Alignment

Eduard Tulchinskii, Laida Kushnareva, Anastasia Voznyuk, Andrei Andriiainen, Irina Piontkovskaya, Evgeny Burnaev +1
EMNLP 2025

Large language models (LLMs) excel at many NLP tasks, yet their multi-step logical reasoning remains unreliable. Existing solutions such as Chain-of-Thought prompting generate intermediate steps but provide no internal check of their logical coherence. In this paper, we use the “QK-score”, a lightwe

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2025

RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks

Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, Serguei Barannikov
AISTATS 2025poster

Topological methods for comparing weighted graphs are valuable in various learning tasks but often suffer from computational inefficiency on large datasets. We introduce RTD-Lite, a scalable algorithm that efficiently compares topological features, specifically connectivity or cluster structures at…

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2024

Robust AI-Generated Text Detection by Restricted Embeddings

Kristian Kuznetsov, Eduard Tulchinskii, Laida Kushnareva, German Magai, Serguei Barannikov, Sergey Nikolenko +1
EMNLP 2024finding

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-ge…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2024

Scalar Function Topology Divergence: Comparing Topology of 3D Objects

Ilya Trofimov*, Daria Voronkova, Eduard Tulchinskii, Evgeny Burnaev, Serguei Barannikov
ECCV 2024poster

"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 dimen…

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2023

Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts

Eduard Tulchinskii, Kristian Kuznetsov, Kushnareva Laida, Daniil Cherniavskii, Sergey Nikolenko, Evgeny Burnaev +2
NeurIPS 2023poster

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 text domains…

Cited by 82Source ↗PDF ↗code ↗Scholar ↗
2023

Learning topology-preserving data representations

Ilya Trofimov, Daniil Cherniavskii, Eduard Tulchinskii, Nikita Balabin, Evgeny Burnaev, Serguei Barannikov
ICLR 2023poster

We propose a method for learning topology-preserving data representations (dimensionality reduction). The method aims to provide topological similarity between the data manifold and its latent representation via enforcing the similarity in topological features (clusters, loops, 2D voids, etc.) and…

Cited by 26Source ↗PDF ↗code ↗Scholar ↗
2022

Acceptability Judgements via Examining the Topology of Attention Maps

Daniil Cherniavskii, Eduard Tulchinskii, Vladislav Mikhailov, Irina Proskurina, Laida Kushnareva, Ekaterina Artemova +4
EMNLP 2022finding

The role of the attention mechanism in encoding linguistic knowledge has received special interest in NLP. However, the ability of the attention heads to judge the grammatical acceptability of a sentence has been underexplored. This paper approaches the paradigm of acceptability judgments with topol…

Cited by 24Source ↗PDF ↗code ↗Scholar ↗