Elamur Research⌕
☰
home / authors / Viktor Moskvoretskii

Viktor Moskvoretskii

Viktor Moskvoretskii has 4 papers in our library, in 2025–2026, most of them on Question answering, Evaluation & benchmarks, Information retrieval & search; often with Alexander Panchenko, Irina Nikishina, Maria Marina.

4 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 3 · 30 works · 33 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 27 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #10195

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

🎓École Polytechnique Fédérale de LausanneLausanne · Switzerland2025–2026 · 12 paperscurrent🎓Skolkovo Institute of Science and TechnologySkolkovo · Russia2024–2026 · 10 paperscurrent🎓National Research University Higher School of EconomicsMoscow · Russia2024–2025 · 8 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 11The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksCognitive Neuroscience · 1 workExperimental and Cognitive Psychology · 1 workNeuroscience · 1 workPhilosophy · 1 workPsychology · 1 workArtificial Intelligence · 3 worksComputer Science · 3 worksLanguage and Linguistics · 2 worksDecision Sciences · 1 workStatistics, Probability and Uncertainty · 1 work

🧭 Topics 3Research subjects and languages the work is about.Information retrieval & search · 1 workEvaluation & benchmarks · 1 workQuestion answering · 1 work

 Works by year

2020s

20263 papers20251 paper

 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. Argument-Based Comparative Question Answering Evaluation Benchmark (2026)
    Large language models can produce fluent answers to comparative questions such as "Simpsons or Family Guy?".
  3. Boosting Self-Consistency with Ranking (2026)
    Self-consistency samples several reasoning chains from an LLM and picks the most frequent answer. The correct answer is often among the samples but loses the vote, leaving a wide gap to oracle selection. The authors propose RISC (Ranking-Improved Self-Consistency), which treats choosing the final answer as a…
  4. Evolutionary Search for Automated Design of Uncertainty Quantification Methods (2026)
    Detecting hallucinations in large language models often relies on uncertainty quantification (UQ) scores. These scores are usually hand-crafted from researchers' intuitions, which limits how far they scale. The authors instead let an LLM invent such scores automatically. They use the EvoTune framework for LLM-driven…

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back HomeAnnual Meeting of the Association for Computational Linguistics (ACL)Viktor Moskvoretskii, Maria Marina, Mikhail Salnikov, et al.2025 · 21 citations
  2. Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning ↗Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) · no summary yetViktor Moskvoretskii, Alexander Panchenko, Irina Nikishina2024 · 12 citations
  3. Argument-Based Comparative Question Answering Evaluation BenchmarkWorkshop on Argument MiningIrina Nikishina, Saba Anwar, Nikolay Dolgov, et al.2026
  4. Boosting Self-Consistency with RankingAnnual Meeting of the Association for Computational Linguistics (ACL)Maria Marina, Daniil Moskovskiy, Sergey Pletenev, et al.2026
  5. Evolutionary Search for Automated Design of Uncertainty Quantification MethodsAnnual Meeting of the Association for Computational Linguistics (ACL)Mikhail Seleznyov, Daniil Korbut, Viktor Moskvoretskii, et al.2026

 Co-authors here

12 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 27 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/viktor-moskvoretskii/