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Artem Vazhentsev

Artem Vazhentsev has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Alexander Panchenko, Ekaterina Fadeeva, Timothy Baldwin.

4 papers in our library · 2025–2026

OpenAlex · h-index 6 · 42 works · 108 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 124 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6966

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

🎓The University of MelbourneMelbourne · Australia2026 · 2 paperscurrent🎓Skolkovo Institute of Science and TechnologySkolkovo · Russia2022–2025 · 3 papers🤝AIRI - Artificial Intelligence Research InstituteMoscow · Russia2023–2024 · 2 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 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksArts and Humanities · 4 worksComputer Science · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3 worksReasoning · 1 work

 Works by year

2020s

20261 paper20253 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph (2025 · 67 citations)
    Uncertainty quantification (UQ) can help flag hallucinated or low-quality outputs of large language models. Earlier work on UQ for LLMs used incompatible methods and evaluation setups, so findings were hard to compare.
  2. Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models (2025 · 13 citations)
    LLMs often produce hallucinated or non-factual output. Many uncertainty quantification (UQ) methods can flag such output, but the strongest ones sample the model repeatedly and are expensive. Density-based scores such as Mahalanobis Distance (MD) are cheap and work well for encoder classifiers, yet earlier…
  3. Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models (2025 · 10 citations)
    LLMs score each new token assuming everything generated so far is correct. If an early token is wrong, the tokens that follow can still look highly confident, so uncertainty estimates built on raw probabilities understate the risk. The authors argue that what matters is a token's unconditional chance of being…
  4. ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning (2026)
    Research on test-time compute (TTC) scaling for LLM reasoning is scattered. Methods are tested under different protocols, model setups and budgets. Most papers report accuracy but not cost, and released code is rarely ready for production. ThinkBooster addresses this with a single open-source framework (MIT license)…

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. Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-PolygraphTransactions of the Association for Computational Linguistics (TACL)Roman Vashurin, Ekaterina Fadeeva, Artem Vazhentsev, et al.2025 · 67 citations
  2. LM-Polygraph: Uncertainty Estimation for Language Models ↗arXiv:2311.07383 · no summary yetEkaterina Fadeeva et al.2023 · 55 citations
  3. Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language ModelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Artem Vazhentsev, Lyudmila Rvanova, Ivan Lazichny, et al.2025 · 13 citations
  4. Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing, et al.2025 · 10 citations
  5. ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Vladislav Smirnov, Quang-Chieu Nguyen, Sergey Senichev, et al.2026

 Co-authors here

12 of 16 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 124 times (of the 149,439 papers whose bibliographies we read); 31 citing papers with a page here →.

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