13 citations · 17 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…
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…
Large Language Models Meet Knowledge Graphs to Answer Factoid Questions
Mikhail Salnikov, Hai Le, Prateek Rajput +4
Recently, it has been shown that the incorporation of structured knowledge into Large Language Models significantly improves the results for a variety of NLP tasks. In this paper,…
Studying Taxonomy Enrichment on Diachronic WordNet Versions
Irina Nikishina, Alexander Panchenko, Varvara Logacheva +1
Ontologies, taxonomies, and thesauri are used in many NLP tasks. However, most studies are focused on the creation of these lexical resources rather than the maintenance of the exi…
RUSSE'2020: Findings of the First Taxonomy Enrichment Task for the Russian language
Irina Nikishina, Varvara Logacheva, Alexander Panchenko +1
This paper describes the results of the first shared task on taxonomy enrichment for the Russian language. The participants were asked to extend an existing taxonomy with previousl…