13 citations · 17 across the 7 of their papers we have counts for
9 papers
Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home
Viktor Moskvoretskii, Maria Lysyuk, Mikhail Salnikov +7
Retrieval Augmented Generation (RAG) improves correctness of Question Answering (QA) and addresses hallucinations in Large Language Models (LLMs), yet greatly increase computationa…
Argument-Based Comparative Question Answering Evaluation Benchmark
Irina Nikishina, Saba Anwar, Nikolay Dolgov +6
In this paper, we aim to solve the problems standing in the way of automatic comparative question answering. To this end, we propose an evaluation framework to assess the quality o…
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…
EBES: Easy Benchmarking for Event Sequences
Dmitry Osin, Igor Udovichenko, Viktor Moskvoretskii +2
Event Sequences (EvS) refer to sequential data characterized by irregular sampling intervals and a mix of categorical and numerical features. Accurate classification of these seque…
GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs
Maxim Zhelnin, Viktor Moskvoretskii, Egor Shvetsov +4
Parameter Efficient Fine-Tuning (PEFT) methods have gained popularity and democratized the usage of Large Language Models (LLMs). Recent studies have shown that a small subset of w…
Low-Resource Machine Translation through the Lens of Personalized Federated Learning
Viktor Moskvoretskii, Nazarii Tupitsa, Chris Biemann +3
We present a new approach called MeritOpt based on the Personalized Federated Learning algorithm MeritFed that can be applied to Natural Language Tasks with heterogeneous data. We…
TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks
Viktor Moskvoretskii, Ekaterina Neminova, Alina Lobanova +2
In this paper, we explore the capabilities of LLMs in capturing lexical-semantic knowledge from WordNet on the example of the LLaMA-2-7b model and test it on multiple lexical seman…
MLEM: Generative and Contrastive Learning as Distinct Modalities for Event Sequences
Viktor Moskvoretskii, Dmitry Osin, Egor Shvetsov +5
This study explores the application of self-supervised learning techniques for event sequences. It is a key modality in various applications such as banking, e-commerce, and health…
IMAD: IMage-Augmented multi-modal Dialogue
Viktor Moskvoretskii, Anton Frolov, Denis Kuznetsov
Currently, dialogue systems have achieved high performance in processing text-based communication. However, they have not yet effectively incorporated visual information, which pos…