ACL 2025finding0 citations

NorEval: A Norwegian Language Understanding and Generation Evaluation Benchmark

Vladislav Mikhailov, Tita Enstad, David Samuel, Hans Christian Farsethås, Andrey Kutuzov, Erik Velldal, Lilja Øvrelid

Abstract

This paper introduces NorEval, a new and comprehensive evaluation suite for large-scale standardized benchmarking of Norwegian generative language models (LMs). NorEval consists of 24 high-quality human-created datasets – of which five are created from scratch. In contrast to existing benchmarks for Norwegian, NorEval covers a broad spectrum of task categories targeting Norwegian language understanding and generation, establishes human baselines, and focuses on both of the official written standards of the Norwegian language: Bokmål and Nynorsk. All our datasets and a collection of over 100 human-created prompts are integrated into LM Evaluation Harness, ensuring flexible and reproducible evaluation. We describe the NorEval design and present the results of benchmarking 19 open-source pretrained and instruction-tuned LMs for Norwegian in various scenarios. Our benchmark, evaluation framework, and annotation materials are publicly available.

BibTeX
@inproceedings{mikhailov-etal-2025-noreval,
    title = "{N}or{E}val: A {N}orwegian Language Understanding and Generation Evaluation Benchmark",
    author = "Mikhailov, Vladislav  and
      Enstad, Tita  and
      Samuel, David  and
      Farseth{\r{a}}s, Hans Christian  and
      Kutuzov, Andrey  and
      Velldal, Erik  and
      {\O}vrelid, Lilja",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.181/",
    doi = "10.18653/v1/2025.findings-acl.181",
    pages = "3495--3541",
    ISBN = "979-8-89176-256-5"
}