Roman Vashurin — accepted AI-conference papers

2026

Don't Throw Away Your Beams: Improving Consistency-based Uncertainties in LLMs via Beam Search

Ekaterina Fadeeva, Maiya Goloburda, Aleksandr Rubashevskii, Roman Vashurin, Artem Shelmanov, Preslav Nakov +2

ICLR 2026poster

Consistency-based methods have emerged as an effective approach to uncertainty quantification (UQ) in large language models. These methods typically rely on several generations obtained via multinomial sampling, measuring their agreement level. However, in short-form QA, multinomial sampling is pron…

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2025

CoCoA: A Minimum Bayes Risk Framework Bridging Confidence and Consistency for Uncertainty Quantification in LLMs

Roman Vashurin, Maiya Goloburda, Albina Ilina, Aleksandr Rubashevskii, Preslav Nakov, Artem Shelmanov +1

NeurIPS 2025poster

Uncertainty quantification for Large Language Models (LLMs) encompasses a diverse range of approaches, with two major families being particularly prominent: (i) information-based, which estimate model confidence from token-level probabilities, and (ii) consistency-based, which assess the semantic ag…

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2025

UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Models

Roman Vashurin, Maiya Goloburda, Preslav Nakov, Maxim Panov

EMNLP 2025

Large Language Models (LLMs) have become indispensable tools across various applications, making it more important than ever to ensure the quality and the trustworthiness of their outputs. This has led to growing interest in uncertainty quantification (UQ) methods for assessing the reliability of LL

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2023

Efficient Out-of-Domain Detection for Sequence to Sequence Models

Artem Vazhentsev, Akim Tsvigun, Roman Vashurin, Sergey Petrakov, Daniil Vasilev, Maxim Panov +2

ACL 2023findings

Sequence-to-sequence (seq2seq) models based on the Transformer architecture have become a ubiquitous tool applicable not only to classical text generation tasks such as machine translation and summarization but also to any other task where an answer can be represented in a form of a finite text frag…

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2022

Embedded Ensembles: infinite width limit and operating regimes

Maksim Velikanov, Roman V. Kail, Ivan Anokhin, Roman Vashurin, Maxim Panov, Alexey Zaytsev +1

AISTATS 2022poster

A memory efficient approach to ensembling neural networks is to share most weights among the ensembled models by means of a single reference network. We refer to this strategy as

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