ltzGLUE: Luxembourgish General Language Understanding Evaluation
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911607880155136 |
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| author | Plum, Alistair Körner, Felicia Lutgen, Anne-Marie Bernardy, Laura Philippy, Fred Milano, Emilia Rehlinger, Nils Lothritz, Cédric Ranasinghe, Tharindu Plank, Barbara Purschke, Christoph |
| author_facet | Plum, Alistair Körner, Felicia Lutgen, Anne-Marie Bernardy, Laura Philippy, Fred Milano, Emilia Rehlinger, Nils Lothritz, Cédric Ranasinghe, Tharindu Plank, Barbara Purschke, Christoph |
| contents | This paper presents ltzGLUE, the first Natural Language Understanding (NLU) benchmark for Luxembourgish (LTZ) based on the popular GLUE benchmark for English. Although NLU tasks are available for many European languages nowadays, LTZ is one of the official national languages that is often overlooked. We construct new tasks and reuse existing ones to introduce the first official NLU benchmark and accompanying evaluation of encoder models for the language. Our tasks include common natural language processing tasks in binary and multi-class classification settings, including named entity recognition, topic classification, and intent classification. We evaluate various pre-trained language models for LTZ to present an overview of the current capabilities of these models on the LTZ language. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17976 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ltzGLUE: Luxembourgish General Language Understanding Evaluation Plum, Alistair Körner, Felicia Lutgen, Anne-Marie Bernardy, Laura Philippy, Fred Milano, Emilia Rehlinger, Nils Lothritz, Cédric Ranasinghe, Tharindu Plank, Barbara Purschke, Christoph Computation and Language This paper presents ltzGLUE, the first Natural Language Understanding (NLU) benchmark for Luxembourgish (LTZ) based on the popular GLUE benchmark for English. Although NLU tasks are available for many European languages nowadays, LTZ is one of the official national languages that is often overlooked. We construct new tasks and reuse existing ones to introduce the first official NLU benchmark and accompanying evaluation of encoder models for the language. Our tasks include common natural language processing tasks in binary and multi-class classification settings, including named entity recognition, topic classification, and intent classification. We evaluate various pre-trained language models for LTZ to present an overview of the current capabilities of these models on the LTZ language. |
| title | ltzGLUE: Luxembourgish General Language Understanding Evaluation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.17976 |