6 papers · 1 filter
Controlling Refusal Behavior of LLMs via Stiefel-Constrained Rotation Steering
Kirill Bunin, Dmitry Bylinkin, Vladimir Aletov +3
Activation steering has emerged as a lightweight approach for controlling model refusal at inference time. A growing line of research explores trainable rotations of activations to…
Leveraging Association Context Retrieval in Knowledge Edit- ing to Build White-Box Attacks on LLMs
Roman Maksimov, Vladimir Aletov, Vladimir Solodkin +3
As large language models (LLMs) are granted increasing autonomy, it is essential to investigate methods that can induce unsafe behavior. We propose a novel white-box attack inspire…
The Kuramoto Neural Operator: Learning to Solve PDEs via Coupled Oscillator Dynamics
Petr Badolia, Leonid Obukhov, Dmitry Bylinkin +1
Operator learning is a rapidly advancing area of computational science. It is particularly well suited to problems where a partial differential equation (PDE) must be solved repeat…
Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning
Dmitriy Bystrov, Daniil Medyakov, Dmitry Bylinkin +1
Fine-tuning large language models (LLMs) has become a central application of modern optimization, enabling pretrained models to adapt to diverse downstream tasks and domain-specifi…
Scalable Knowledge Editing for Mixture-of-Experts LLMs via Tensor-Structured Updates
Roman Maksimov, Vladimir Aletov, Dmitry Bylinkin +3
Knowledge editing (KE) provides a lightweight alternative to repeated fine-tuning of LLMs. However, most existing KE methods target dense feed-forward layers, while modern LLMs inc…
Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems
Dmitry Bylinkin, Sergey Skorik, Dmitriy Bystrov +3
Heterogeneity within data distribution poses a challenge in many modern federated learning tasks. We formalize it as an optimization problem involving a computationally heavy compo…