AIConfPaper
← Search

Nurdaulet Mukhituly

4 accepted papers

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…

Cited by 0Source ↗PDF ↗Scholar ↗
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…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models

Margaret Mitchell, Giuseppe Attanasio, Ioana Baldini, Miruna Clinciu, Jordan Clive, Pieter Delobelle +48
NAACL 2025long

Large Language Models (LLMs) reproduce and exacerbate the social biases present in their training data, and resources to quantify this issue are limited. While research has attempted to identify and mitigate such biases, most efforts have been concentrated around English, lagging the rapid advanceme…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

SPIRIT: Patching Speech Language Models against Jailbreak Attacks

Amirbek Djanibekov, Nurdaulet Mukhituly, Kentaro Inui, Hanan Aldarmaki, Nils Lukas
EMNLP 2025

Speech Language Models (SLMs) enable natural interactions via spoken instructions, which more effectively capture user intent by detecting nuances in speech. The richer speech signal introduces new security risks compared to text-based models, as adversaries can better bypass safety mechanisms by in

Cited by 0Source ↗PDF ↗Scholar ↗