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Vasily Konovalov

Vasily Konovalov has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Lexical semantics & word embeddings, Legal AI; often with Alexander Panchenko, Nikolay Ivanov, Daria Galimzianova.

4 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 6 · 50 works · 84 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 15 times in our library · ⭐ Paper Stars ranking: #8981

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home (2025 · 21 citations)
    Adaptive retrieval lets an LLM decide for each question whether it needs external documents. The goal is to keep the accuracy benefits of RAG without paying for retrieval that is unnecessary or even misleading. Recent pipelines for this task are usually judged on answer quality and retrieval counts. They rarely…
  2. DeepPavlov Strikes Back: A Toolkit for Improving LLM Reliability and Trustworthiness (2026)
    DeepPavlov 1.1 adapts the long-running open-source DeepPavlov framework to LLM-centric pipelines. LLM systems with retrieval can produce unfaithful, unnecessary or harmful output, and established toolkits such as spaCy, Stanza and Flair offer no components for checking this. The release keeps the classical linguistic…
  3. Fine-Grained Semantic Comparison of Legal Documents using LLMs (2026)
    Legal and regulatory documents in large organizations are revised often, and these revisions can introduce contradictions that reviewers miss.
  4. Using BERT to Explore Lexical Semantic Change of Prepositions (2026)
    Prepositions are very polysemous and sit between grammatical and lexical words, yet NLP has given them little attention.

 Co-authors here

9 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 15 times (of the 149,439 papers whose bibliographies we read); 4 citing papers with a page here →.

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