Active Learning for Abstractive Text Summarization. - Akim Tsvigun; Ivan Lysenko; Danila Sedashov; Ivan Lazichny; Eldar Damirov; Vladimir Karlov; Artemy Belousov; Leonid Sanochkin; Maxim Panov; Alexander Panchenko; Mikhail Burtsev 001; Artem Shelmanov (EMNLP (Findings), 2022)

ALToolbox: A Set of Tools for Active Learning Annotation of Natural Language Texts. - Akim Tsvigun; Leonid Sanochkin; Daniil Larionov; Gleb Kuzmin; Artem Vazhentsev; Ivan Lazichny; Nikita Khromov; Danil Kireev; Aleksandr Rubashevskii; Olga Shahmatova (EMNLP (Demos), 2022)

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)

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)

Geopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models. - Mikhail Salnikov; Dmitrii Korzh; Ivan Lazichny; Elvir Karimov; Artyom Iudin; Ivan V. Oseledets; Oleg Y. Rogov; Natalia V. Loukachevitch; Alexander Panchenko; Elena Tutubalina (CoRR, 2025)