Papers by Alexey Dontsov
CLEAR: Character Unlearning in Textual and Visual Modalities (2025.findings-acl)
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Alexey Dontsov, Dmitrii Korzh, Alexey Zhavoronkin, Boris Mikheev, Denis Bobkov, Aibek Alanov, Oleg Rogov, Ivan Oseledets, Elena Tutubalina
| Challenge: | Existing methods for removing private or hazardous data from deep learning models are focused on single-modality models. |
| Approach: | They propose CLEAR, the first open-source benchmark specifically for MMU. CLEAR contains 200 fictitious individuals and 3,700 images linked with corresponding question-answer pairs. |
| Outcome: | The proposed benchmarks show that unlearning both modalities outperform single-modality approaches. |
Out of Distribution, Out of Luck: Process Rewards Misguide Reasoning Models (2026.eacl-short)
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| Challenge: | 80% of reasoning model outputs respond to formatting artifacts rather than mathematical content. |
| Approach: | They evaluate process reward models that provide step-level feedback during inference . they identify distinct reward prediction patterns that differentiate reasoning from non-reasoning model outputs . |
| Outcome: | The proposed model fails to enhance and sometimes degrade reasoning model performance. |
Feature Drift: How Fine-Tuning Repurposes Representations in LLMs (2026.findings-eacl)
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| Challenge: | Sparse autoencoders (SAEs) are a powerful tool for interpreting neural networks by extracting concepts (features) represented in their activations. |
| Approach: | They propose to use Sparse Autoencoders to extract concepts from their activations to explain how fine-tuning changes model capabilities. |
| Outcome: | The proposed model recombines existing concepts rather than learning new ones, and shows that it is a better explanation for how fine-tuning changes model capabilities. |
Motivating Next-Gen Accelerators with Flexible N:M Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches (2026.acl-industry)
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Shirin Alanova, Kristina Kazistova, Ekaterina Galaeva, Alina Kostromina, Vladimir Smirnov, Redko Dmitry, Alexey Dontsov, Maxim Zhelnin, Evgeny Burnaev, Egor Shvetsov
| Challenge: | Recent studies show that sparsification is not supported in large language models. |
| Approach: | They propose to use activation pruning to accelerate large language models with sparsification . they compare activation pruners with weight pruner and activater pruning with activation . |
| Outcome: | The proposed approach outperforms weight pruning at matched sparsity levels. |