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Maxim Panov

Maxim Panov has 3 papers in our library, in 2024–2025, most of them on Language models & pretraining; often with Alexander Panchenko, Artem Shelmanov, Timothy Baldwin.

3 papers in our library · 2024–2025

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📄 3 works in our library · 🔗 Cited 88 times in our library · ⭐ Paper Stars ranking: #7577

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

 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 · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksSocial Sciences · 1 workSociology and Political Science · 1 work

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

 Works by year

2020s

20252 papers20241 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. 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…

 Co-authors here

12 of 14 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 88 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

Updated

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