4 citations · 28 across the 86 of their papers we have counts for
8 papers · 2 filters
CLARITY: Clinical Assistant for Routing, Inference, and Triage
Vladimir Shaposhnikov, Aleksandr Nesterov, Ilia Kopanichuk +7
We present CLARITY (Clinical Assistant for Routing, Inference and Triage), an AI-driven platform designed to facilitate patient-to-specialist routing, clinical consultations, and s…
Geopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models
Mikhail Salnikov, Dmitrii Korzh, Ivan Lazichny +7
This paper evaluates geopolitical biases in LLMs with respect to various countries though an analysis of their interpretation of historical events with conflicting national perspec…
Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models
Pengyi Li, Matvey Skripkin, Alexander Zubrey +2
Large language models (LLMs) excel at reasoning, yet post-training remains critical for aligning their behavior with task goals. Existing reinforcement learning (RL) methods often…
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…
Exploring the Hidden Capacity of LLMs for One-Step Text Generation
Gleb Mezentsev, Ivan Oseledets
A recent study showed that large language models (LLMs) can reconstruct surprisingly long texts - up to thousands of tokens - via autoregressive generation from just one trained in…
On the Spatial Structure of Mixture-of-Experts in Transformers
Daniel Bershatsky, Ivan Oseledets
A common assumption is that MoE routers primarily leverage semantic features for expert selection. However, our study challenges this notion by demonstrating that positional token…
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…
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…