6 citations · 7 across the 5 of their papers we have counts for
5 papers
Talking to Data: Designing Smart Assistants for Humanities Databases
Alexander Sergeev, Valeriya Goloviznina, Mikhail Melnichenko +1
Access to humanities research databases is often hindered by the limitations of traditional interaction formats, particularly in the methods of searching and response generation. T…
Do LLMs Understand Why We Write Diaries? A Method for Purpose Extraction and Clustering
Valeriya Goloviznina, Alexander Sergeev, Mikhail Melnichenko +1
Diary analysis presents challenges, particularly in extracting meaningful information from large corpora, where traditional methods often fail to deliver satisfactory results. This…
I've got the "Answer"! Interpretation of LLMs Hidden States in Question Answering
Valeriya Goloviznina, Evgeny Kotelnikov
Interpretability and explainability of AI are becoming increasingly important in light of the rapid development of large language models (LLMs). This paper investigates the interpr…
Automatic Summarization of Russian Texts: Comparison of Extractive and Abstractive Methods
Valeriya Goloviznina, Evgeny Kotelnikov
The development of large and super-large language models, such as GPT-3, T5, Switch Transformer, ERNIE, etc., has significantly improved the performance of text generation. One of…
Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts
Irina Fishcheva, Valeriya Goloviznina, Evgeny Kotelnikov
Argumentation mining is a field of computational linguistics that is devoted to extracting from texts and classifying arguments and relations between them, as well as constructing…