Kazakhstani research at ACL 2025

The conference featured work by our compatriots and community residents. These papers expand the research foundation for building and evaluating LLMs in low-resource languages, with a particular focus on Kazakh:

• Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh — Nurkhan Laiyk, Daniil Orel, Maiya Goloburda
• KazMMLU: Evaluating Language Models on Kazakh, Russian, and Regional Knowledge of Kazakhstan — Mukhammed Togmanov, Nurdaulet Mukhituly, Diana Turmakhan, Maiya Goloburda, Bekassyl Syzdykov, Nurkhan Laiyk
• Qorgau: Evaluating LLM Safety in Kazakh-Russian Bilingual Contexts — Maiya Goloburda, Nurkhan Laiyk, Diana Turmakhan, Mukhammed Togmanov, Askhat Sametov, Nurdaulet Mukhituly, Daniil Orel
• CoDet-M4: Detecting Machine-Generated Code in Multi-Lingual, Multi-Generator and Multi-Domain Settings — Daniil Orel
• RECALL: Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles — Zangir Iklassov

This is a landmark moment for Kazakhstan's entire AI community. Congratulations to the authors on reaching the global stage—we look forward to their next achievements!