13 citations · 20 across the 11 of their papers we have counts for
11 papers · 1 filter
Context or Knowledge: How LLMs Attribute Their Own Answers
Kirill Afendulev, Alexey Dontsov, Alla Chepurova +2
Language models can answer a question from a supplied document or from their parametric knowledge. When later asked where the answer came from, what determines their source report?…
From Stories to Features: Scaling Concept Vectors for AI Alignment & Interpretability
Dmitry Scherbakov, Oleg Chernikov, Victoria Khamash +5
We study feature vectors: directions in a language model's activation space that correspond to human-interpretable concepts, such as honesty or deception. Existing unsupervised met…
LANTERN: Illuminating Hidden Mathematical Knowledge in Language Models
Pavel Tikhonov, Elena Tutubalina, Ivan Oseledets +2
Language models can now prove theorems, but people still decide which problems to pursue. We ask whether a model's internal representations can help identify promising mathematical…
Your Transformer Can Hold Two Thoughts at Once: Evidence of Linear Superposition in LLMs
Pavel Tikhonov, Anton Korznikov, Matvey Mikhalchuk +6
While Large Language Models (LLMs) rely on highly non-linear components, in this work we demonstrate that they exhibit fundamental linearity: when inputs from distinct text streams…
What Attention Recalls and Recurrence Controls in Hybrid Language Models
Kirill Afendulev, Alexey Dontsov, Elena Tutubalina +1
Hybrid language models combine attention with a fixed-size recurrent state, but the role of each channel remains unclear. We introduce two cache-level interventions. Split-prefill…
Anatomy of Unlearning: The Dual Impact of Fact Salience and Model Fine-Tuning
Anna Borisiuk, Andrey Savchenko, Alexander Panchenko +1
Machine Unlearning (MU) enables Large Language Models (LLMs) to remove unsafe or outdated information. However, existing work assumes that all facts are equally forgettable and lar…
HAMSA: Hijacking Aligned Compact Models via Stealthy Automation
Alexey Krylov, Iskander Vagizov, Dmitrii Korzh +6
Large Language Models (LLMs), especially their compact efficiency-oriented variants, remain susceptible to jailbreak attacks that can elicit harmful outputs despite extensive align…
Team Anotheroption at SemEval-2025 Task 8: Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA
Nikolas Evkarpidi, Elena Tutubalina
This paper presents a system developed for SemEval 2025 Task 8: Question Answering (QA) over tabular data. Our approach integrates several key components: text-to-SQL and text-to-c…
One Task Vector is not Enough: A Large-Scale Study for In-Context Learning
Pavel Tikhonov, Ivan Oseledets, Elena Tutubalina
In-context learning (ICL) enables Large Language Models (LLMs) to adapt to new tasks using few examples, with task vectors - specific hidden state activations - hypothesized to enc…
I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders
Andrey Galichin, Alexey Dontsov, Polina Druzhinina +4
Recent LLMs like DeepSeek-R1 have demonstrated state-of-the-art performance by integrating deep thinking and complex reasoning during generation. However, the internal mechanisms b…
Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian
Tatiana Shamardina, Vladislav Mikhailov, Daniil Chernianskii +7
We present the shared task on artificial text detection in Russian, which is organized as a part of the Dialogue Evaluation initiative, held in 2022. The shared task dataset includ…