1 citations · 1 across the 7 of their papers we have counts for
7 papers
MindShift: Analyzing Language Models' Reactions to Psychological Prompts
Anton Vasiliuk, Irina Abdullaeva, Polina Druzhinina +2
Large language models (LLMs) hold the potential to absorb and reflect personality traits and attitudes specified by users. In our study, we investigated this potential using robust…
Real-World Transferable Adversarial Attack on Face-Recognition Systems
Andrey Kaznacheev, Matvey Mikhalchuk, Andrey Kuznetsov +2
Adversarial attacks on face recognition (FR) systems pose a significant security threat, yet most are confined to the digital domain or require white-box access. We introduce GaP (…
SONAR-LLM: Autoregressive Transformer that Thinks in Sentence Embeddings and Speaks in Tokens
Nikita Dragunov, Temurbek Rahmatullaev, Elizaveta Goncharova +5
The recently proposed Large Concept Model (LCM) generates text by predicting a sequence of sentence-level embeddings and training with either mean-squared error or diffusion object…
Inverting Black-Box Face Recognition Systems via Zero-Order Optimization in Eigenface Space
Anton Razzhigaev, Matvey Mikhalchuk, Klim Kireev +3
Reconstructing facial images from black-box recognition models poses a significant privacy threat. While many methods require access to embeddings, we address the more challenging…
LLM-Microscope: Uncovering the Hidden Role of Punctuation in Context Memory of Transformers
Anton Razzhigaev, Matvey Mikhalchuk, Temurbek Rahmatullaev +4
We introduce methods to quantify how Large Language Models (LLMs) encode and store contextual information, revealing that tokens often seen as minor (e.g., determiners, punctuation…
Universal Adversarial Attack on Aligned Multimodal LLMs
Temurbek Rahmatullaev, Polina Druzhinina, Nikita Kurdiukov +3
We propose a universal adversarial attack on multimodal Large Language Models (LLMs) that leverages a single optimized image to override alignment safeguards across diverse queries…
Logit-KL Flow Matching: Non-Autoregressive Text Generation via Sampling-Hybrid Inference
Egor Sevriugov, Nikita Dragunov, Anton Razzhigaev +2
Non-autoregressive (NAR) language models offer notable efficiency in text generation by circumventing the sequential bottleneck of autoregressive decoding. However, accurately mode…