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Evgenii Tsymbalov

Evgenii Tsymbalov has 2 papers in our library, in 2024–2026; often with Alexander Panchenko, Artem Shelmanov, Ekaterina Fadeeva.

2 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 12 · 32 works · 462 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 93 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6887

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

🎓Skolkovo Institute of Science and TechnologySkolkovo · Russia2017–2022 · 13 papers🎓National Research University Higher School of EconomicsMoscow · Russia2018 · 2 papers

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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSocial Sciences · 1 workSociology and Political Science · 1 work

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification (2024 · 96 citations)
    LLMs often slip false statements into otherwise accurate text, and readers rarely notice them. The authors propose a fact-checking pipeline that needs no external knowledge base. The output is split into atomic claims (as in FactScore, via the OpenAI Chat API), and each claim is aligned with its tokens. Token-level…
  2. Detecting Hallucinations in SpeechLLMs at Inference Time Using Attention Maps (2026)
    SpeechLLMs sometimes produce fluent output that is not supported by the audio. Most existing ways to catch these hallucinations need reference transcripts, which are not available during deployment. The authors propose a reference-free detector that runs at inference time and reads the model's own attention maps.…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Fact-Checking the Output of Large Language Models via Token-Level Uncertainty QuantificationFindings of the Association for Computational LinguisticsEkaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, et al.2024 · 96 citations
  2. ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change ↗arXiv:2401.09646 · no summary yetDavid Thulke et al.2024 · 45 citations
  3. Detecting Hallucinations in SpeechLLMs at Inference Time Using Attention MapsFindings of the Association for Computational LinguisticsJonas Waldendorf, Bashar Awwad Shiekh Hasan, Evgenii Tsymbalov2026

 Co-authors here

11 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 93 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated

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