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Maiya Goloburda

6 accepted 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

Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh

Nurkhan Laiyk, Daniil Orel, Rituraj Joshi, Maiya Goloburda, Yuxia Wang, Preslav Nakov +1
ACL 2025long

Instruction tuning in low-resource languages remains underexplored due to limited text data, particularly in government and cultural domains. To address this, we introduce and open-source a large-scale (10,600 samples) instruction-following (IFT) dataset, covering key institutional and cultural know…

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2025

KazMMLU: Evaluating Language Models on Kazakh, Russian, and Regional Knowledge of Kazakhstan

Mukhammed Togmanov, Nurdaulet Mukhituly, Diana Turmakhan, Jonibek Mansurov, Maiya Goloburda, Akhmed Sakip +8
ACL 2025long

Despite having a population of twenty million, Kazakhstan’s culture and language remain underrepresented in the field of natural language processing. Although large language models (LLMs) continue to advance worldwide, progress in Kazakh language has been limited, as seen in the scarcity of dedicate…

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2025

Qorǵau: Evaluating Safety in Kazakh-Russian Bilingual Contexts

Maiya Goloburda, Nurkhan Laiyk, Diana Turmakhan, Yuxia Wang, Mukhammed Togmanov, Jonibek Mansurov +8
ACL 2025finding

Large language models (LLMs) are known to have the potential to generate harmful content, posing risks to users. While significant progress has been made in developing taxonomies for LLM risks and safety evaluation prompts, most studies have focused on monolingual contexts, primarily in English. How…

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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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