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cs.CL2025
Enhancing BERT Fine-Tuning for Sentiment Analysis in Lower-Resourced Languages
Jozef Kubík, Marek Šuppa, Martin Takáč
Limited data for low-resource languages typically yield weaker language models (LMs). Since pre-training is compute-intensive, it is more pragmatic to target improvements during fi…
cs.CL2025
skLEP: A Slovak General Language Understanding Benchmark
Marek Šuppa, Andrej Ridzik, Daniel Hládek +5
In this work, we introduce skLEP, the first comprehensive benchmark specifically designed for evaluating Slovak natural language understanding (NLU) models. We have compiled skLEP…
cs.CL2025
Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation
Israfel Salazar, Manuel Fernández Burda, Shayekh Bin Islam +42
The evaluation of vision-language models (VLMs) has mainly relied on English-language benchmarks, leaving significant gaps in both multilingual and multicultural coverage. While mu…