1 citations · 1 across the 5 of their papers we have counts for
5 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…
Konstruktor: A Strong Baseline for Simple Knowledge Graph Question Answering
Maria Lysyuk, Mikhail Salnikov, Pavel Braslavski +1
While being one of the most popular question types, simple questions such as "Who is the author of Cinderella?", are still not completely solved. Surprisingly, even the most powerf…
Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs
Mikhail Salnikov, Maria Lysyuk, Pavel Braslavski +3
Pre-trained Text-to-Text Language Models (LMs), such as T5 or BART yield promising results in the Knowledge Graph Question Answering (KGQA) task. However, the capacity of the model…
Error syntax aware augmentation of feedback comment generation dataset
Nikolay Babakov, Maria Lysyuk, Alexander Shvets +2
This paper presents a solution to the GenChal 2022 shared task dedicated to feedback comment generation for writing learning. In terms of this task given a text with an error and a…
Tensor-based Collaborative Filtering With Smooth Ratings Scale
Nikita Marin, Elizaveta Makhneva, Maria Lysyuk +3
Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception. Some users may rarely give 5 stars to items whi…