Ivan Lazichny

Ivan Lazichny has 2 papers in our library, in 2025, most of them on Language models & pretraining; often with Alexander Panchenko, Artem Shelmanov, Artem Vazhentsev.

2 papers in our library · 2025

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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…
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…

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Papers of our library cite their works 20 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

Updated 31 October 2026