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Daniil Orel

5 accepted papers

2025

CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation

Emilio Villa-Cueva, Sholpan Bolatzhanova, Diana Turmakhan, Kareem Elzeky, Henok Biadglign Ademtew, Alham Fikri Aji +29
EMNLP 2025

Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey sufficient context to capture region-specific meanings. In this work, we investigate whether images can act as cultural

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2025

CoDet-M4: Detecting Machine-Generated Code in Multi-Lingual, Multi-Generator and Multi-Domain Settings

Daniil Orel, Dilshod Azizov, Preslav Nakov
ACL 2025finding

Large Language Models (LLMs) have revolutionized code generation, automating programming with remarkable efficiency. However, this has had important consequences for programming skills, ethics, and assessment integrity, thus making the detection of LLM-generated code essential for maintaining accoun…

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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

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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