Lyudmila Rvanova

Lyudmila Rvanova has 2 papers in our library, in 2025, most of them on Language models & pretraining; often with Alexander Panchenko, Artem Vazhentsev, Timothy Baldwin.

2 papers in our library · 2025

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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.
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…

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Papers of our library cite their works 61 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated 30 October 2026