15 citations · 19 across the 12 of their papers we have counts for
8 papers · 1 filter
How to model Human Actions distribution with Event Sequence Data
Egor Surkov, Dmitry Osin, Evgeny Burnaev +1
This paper studies forecasting of the future distribution of events in human action sequences, a task essential in domains like retail, finance, healthcare, and recommendation syst…
Geometric Metrics and LLMs: What They Measure and When They Work
Viacheslav Yusupov, Anna Antipina, Ameliia Alaeva +6
We present a systematic stress-test of geometric metrics for LLM evaluation. Rank-based geometric properties of internal representations have shown promise as reference-free qualit…
Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches
Shirin Alanova, Kristina Kazistova, Ekaterina Galaeva +7
The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N:M) pruning is well-established for we…
Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs
Maxim Zhelnin, Dmitry Redko, Daniil Volkov +8
Sequential recommendations (SR) with transformer-based architectures are widely adopted in real-world applications, where SR models require frequent retraining to adapt to ever-cha…
Token Homogenization under Positional Bias
Viacheslav Yusupov, Danil Maksimov, Ameliia Alaeva +9
This paper investigates token homogenization - the convergence of token representations toward uniformity across transformer layers and its relationship to positional bias in large…
From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction
Egor Maximov, Yulia Kuzkina, Azamat Kanametov +4
As large language models (LLMs) grow in size, efficient compression techniques like quantization and sparsification are critical. While quantization maintains performance with redu…
Beyond Early-Token Bias: Model-Specific and Language-Specific Position Effects in Multilingual LLMs
Mikhail Menschikov, Alexander Kharitonov, Maiia Kotyga +5
Large Language Models (LLMs) exhibit position bias systematically underweighting information based on its location in the context but how this bias varies across languages and mode…
Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models
Artyom Kharinaev, Viktor Moskvoretskii, Egor Shvetsov +3
Large Language Models (LLMs) are powerful tools for modern applications, but their computational demands limit accessibility. Quantization offers efficiency gains, yet its impact o…