20.500.12556/RUL-182550 Mono- and cross-lingual evaluation of representation language models on less-resourced languages The current dominance of large language models in natural language processing is based on their contextual awareness. For text classification, text representation models, such as ELMo, BERT, and BERT derivatives, are typically fine-tuned for a specific problem. Most existing work focuses on English; in contrast, we present a large-scale multilingual empirical comparison of several monolingual and multilingual ELMo and BERT models using 14 classification tasks in nine languages. The results show, that the choice of best model largely depends on the task and language used, especially in a cross-lingual setting. In monolingual settings, monolingual BERT models tend to perform the best among BERT models. Among ELMo models, the ones trained on large corpora dominate. Cross-lingual knowledge transfer is feasible on most tasks already in a zero-shot setting without losing much performance. monolingual models multilingual models ELMo BERT corpus cross-lingual datasets language models contextual embeddings less-resourced languages BERT ELMo korpusi večjezični veliki modeli true false true Angleški jezik Slovenski jezik Članek v reviji 2026-05-15 14:42:47 2026-05-15 14:42:51 2026-05-16 04:59:45 0000-00-00 00:00:00 2026 0 0 29 str. Vol. 95, art. 101852 Jan. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8 1095-8363 10.1016/j.csl.2025.101852 241622275 1-s2.0-S0885230825000774-main.pdf 1-s2.0-S0885230825000774-main.pdf 1 959E090AF6BB03C8064D9ED465BAB5B6 a4dc0887e10b1e7b7fb837a87e876b5196ac73bbbd8b7a0b5ca11410fe34e01b a35e8f78-505b-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=233922 https://www.sciencedirect.com/science/article/pii/S0885230825000774 0 49103a33-505b-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=233921 Fakulteta za računalništvo in informatiko 0 0 0