Automatic Structuring of Topics for Natural Language Generation in Community Question Answering in Programming Domain. - Lyudmila Rvanova; Sergey V. Kovalchuk (ICCS (2), 2023)

Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph. - Roman Vashurin; Ekaterina Fadeeva; Artem Vazhentsev; Akim Tsvigun; Daniil Vasilev; Rui Xing; Abdelrahman Boda Sadallah; Lyudmila Rvanova; Sergey Petrakov; Alexander Panchenko; Timothy Baldwin; Preslav Nakov; et al. (CoRR, 2024)

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models. - Artem Vazhentsev; Lyudmila Rvanova; Ivan Lazichny; Alexander Panchenko; Maxim Panov; Timothy Baldwin; Artem Shelmanov (NAACL (Long Papers), 2025)

Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph. - Roman Vashurin; Ekaterina Fadeeva; Artem Vazhentsev; Lyudmila Rvanova; Daniil Vasilev; Akim Tsvigun; Sergey Petrakov; Rui Xing; Abdelrahman Boda Sadallah; Kirill Grishchenkov; Alexander Panchenko; Timothy Baldwin; et al. (Trans. Assoc. Comput. Linguistics, 2025)

Uncertainty-Aware Attention Heads: Efficient Unsupervised Uncertainty Quantification for LLMs. - Artem Vazhentsev; Lyudmila Rvanova; Gleb Kuzmin; Ekaterina Fadeeva; Ivan Lazichny; Alexander Panchenko; Maxim Panov; Timothy Baldwin; Mrinmaya Sachan; Preslav Nakov; Artem Shelmanov (CoRR, 2025)