Tokenization is presented as a core language-model design choice that shapes sequence length, computational cost, multilingual equity, evaluation, and security—not merely as preprocessing. The...

Multilingual large language models (LLMs) have been shown to perform better on non-English classification tasks when the representations of the given language are more aligned to English within the mo...

Lukáš Eigler, Jindřich Libovický, David Hurych. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop). 2026.

Dušan Variš, Abishek Stephen, Jindřich Libovický. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations). 2026.

We present CHALIS (Challenging Language Identification Samples), a new benchmark dataset explicitly designed to address difficult cases in language identification: cousin languages and orthographic no...

Validating evaluation metrics for NLG typically relies on expensive and time-consuming human annotations, which predominantly exist only for English datasets. We propose LLM as a Meta-Judge, a scalabl...

Three years ago, I took the introductory machine learning course over from Milan Straka, and one of the problems I had to deal with was: how do I grade 250 written exams without it consuming my entire...

We introduce CUS-QA, a benchmark for evaluation of open-ended regional question answering that encompasses both textual and visual modalities. We also provide strong baselines using state-of-the-art l...

We present a novel metric for the evaluation of the morphological plausibility of subword segmentation. Unlike the typically used morpheme boundary or retrieval F-score, which requires gold segmentati...

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