1 citations · 4 across the 10 of their papers we have counts for
11 papers
Why Don't You Know? Evaluating the Impact of Uncertainty Sources on Uncertainty Quantification in LLMs
Maiya Goloburda, Roman Vashurin, Fedor Chernogorskii +4
As Large Language Models (LLMs) are increasingly deployed in real-world applications, reliable uncertainty quantification (UQ) becomes critical for safe and effective use. Most exi…
Don't Throw Away Your Beams: Improving Consistency-based Uncertainties in LLMs via Beam Search
Ekaterina Fadeeva, Maiya Goloburda, Aleksandr Rubashevskii +5
Consistency-based methods have emerged as an effective approach to uncertainty quantification (UQ) in large language models. These methods typically rely on several generations obt…
Multidimensional Uncertainty Quantification via Optimal Transport
Nikita Kotelevskii, Maiya Goloburda, Vladimir Kondratyev +4
Most uncertainty quantification (UQ) approaches provide a single scalar value as a measure of model reliability. However, different uncertainty measures could provide complementary…
UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Models
Roman Vashurin, Maiya Goloburda, Preslav Nakov +1
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…
Sherkala-Chat: Building a State-of-the-Art LLM for Kazakh in a Moderately Resourced Setting
Fajri Koto, Rituraj Joshi, Nurdaulet Mukhituly +31
Llama-3.1-Sherkala-8B-Chat, or Sherkala-Chat (8B) for short, is a state-of-the-art instruction-tuned open generative large language model (LLM) designed for Kazakh. Sherkala-Chat (…
GenAI Content Detection Task 1: English and Multilingual Machine-Generated Text Detection: AI vs. Human
Yuxia Wang, Artem Shelmanov, Jonibek Mansurov +23
We present the GenAI Content Detection Task~1 -- a shared task on binary machine generated text detection, conducted as a part of the GenAI workshop at COLING 2025. The task consis…
Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh
Nurkhan Laiyk, Daniil Orel, Rituraj Joshi +4
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…
Qorgau: Evaluating LLM Safety in Kazakh-Russian Bilingual Contexts
Maiya Goloburda, Nurkhan Laiyk, Diana Turmakhan +11
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 f…
KazMMLU: Evaluating Language Models on Kazakh, Russian, and Regional Knowledge of Kazakhstan
Mukhammed Togmanov, Nurdaulet Mukhituly, Diana Turmakhan +11
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…
Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI
Yuxia Wang, Rui Xing, Jonibek Mansurov +23
Prior studies have shown that distinguishing text generated by Large Language Models (LLMs) from human-written one is highly challenging for humans, and often no better than random…
Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency
Roman Vashurin, Maiya Goloburda, Albina Ilina +4
Uncertainty quantification (UQ) methods for Large Language Models (LLMs) encompass a variety of approaches, with two major types being particularly prominent: information-based, wh…