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Publications

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Highlights

  • MMTEB: Massive Multilingual Text Embedding Benchmark

    MMTEB was a large scale community collaboration of more than 50 authors to expands embedding benchmark MTEB to expand the multilingual coverage to more than 250 languages, while significantly reducing inference cost. It is today one of the de-facto bencharks for embeddings.

    Code Paper Blog

  • Dynaword: From One-shot to Continuously Developed Datasets

    Dynaword is a framework for continously expanding open datasets of training data for large language models. We prove that this framework works by providing an implementation of it, the Danish Dynaword, which have grown by more than 6x in less than a year.

    Data Paper

  • Dacy: A Unified Framework for Danish NLP

    DaCy is a unified framework for Danish NLP which still todays (2026) offer state-of-the-art performance for Danish Named entity recognition, dependency parsing, parts-of-speech tagging and more.

    Code Paper

Peer-reviewed and Accepted

Sorted by year, but otherwise in no particular order

  • Enevoldsen, K., Jensen, K. N., Kostkan, J., Szabó, B., Kardos, M., Vad, K., Heinsen, J., Núñez, A. B., Barmina, G., Nielsen, J., Larsen, R., Vahlstrup, P. B., & others. (2026). Dynaword: From one-shot to continuously developed datasets. Proceedings of the Language Resources and Evaluation Conference (LREC). https://arxiv.org/abs/2508.02271
  • El Assadi, A., Chung, I., Solomatin, R., Muennighoff, N., & Enevoldsen, K. (2026). HUME: Measuring the human-model performance gap in text embedding tasks. International Conference on Learning Representations (ICLR).
  • Lyngbaek, L., Feldkamp, P., Bizzoni, Y., Nielbo, K., & Enevoldsen, K. (2026). Is sentiment banana-shaped? Exploring the geometry and portability of sentiment concept vectors. Proceedings of the 15th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA), 146–160. Association for Computational Linguistics.
  • Jacomy, M., Rieder, B., Borra, E., Plique, G., Enevoldsen, K. C., & van Eck, N. J. (2026). Toolmaking is science by other means (pp. 294–306); Meet the DCSSH toolmakers (pp. 307–313); Why do DCSSH toolmakers make tools? (pp. 314–324); The lifecycle of DC tools for SSH (pp. 325–338); Valuing toolmaking in academia (pp. 339–348); In defense of the researcher-toolmaker figure (pp. 349–351). In Handbook of Digital and Computational Research Methods. Edward Elgar Publishing.
  • Enevoldsen, K., Chung, I., Mathur, A., Kerboua, I., Kardos, M., Stap, D., Gala, J., Siblini, W., Sturua, S., Utpala, S., Sequeira, G., Schaeffer, M., Ciancone, M., Misra, D., Dhakal, S., Rystrøm, J., Weller, O., Xiao, C., Çag, Ö., … Cassano, F. (2025). MMTEB: Massive Multilingual Text Embedding Benchmark. International Conference on Learning Representations (ICLR).
  • Kardos, M., Kostkan, J., Enevoldsen, K., Vermillet, A.-Q., Nielbo, K., & Rocca, R. (2025). S³ — Semantic Signal Separation. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL), Volume 1: Long Papers, 633–666.
  • Lyngbaek, L., Feldkamp, P., Bizzoni, Y., Nielbo, K., & Enevoldsen, K. (2025). Continuous sentiment scores for literary and multilingual contexts. Computational Humanities Research (CHR), 3, 480–497.
  • Kardos, M., Enevoldsen, K. C., Kostkan, J., Kristensen-McLachlan, R. D., & Rocca, R. (2025). Turftopic: Topic modelling with contextual representations from sentence transformers. Journal of Open Source Software, 10(111), 8183.
  • Xiao, C., Chung, I., Kerboua, I., Stirling, J., Zhang, X., Kardos, M., Solomatin, R., Moubayed, N. A., Enevoldsen, K., & Muennighoff, N. (2025). MIEB: Massive image embedding benchmark. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 22187–22198.
  • Chung, I., Kerboua, I., Kardos, M., Solomatin, R., & Enevoldsen, K. (2025). Maintaining MTEB: Towards long term usability and reproducibility of embedding benchmarks. Championing Open-Source Development in ML Workshop @ ICML25. https://openreview.net/forum?id=qcPJs0KRZW
  • Kardos, M., Enevoldsen, K. C., & Nielbo, K. L. (2025). Topicwizard – a modern, model-agnostic framework for topic model visualization and interpretation. Proceedings of the 8th International Conference on Natural Language and Speech Processing (ICNLSP), 19–27.
  • Nielsen, D. S., Enevoldsen, K., & Schneider-Kamp, P. (2025). Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU Tasks. Nordic Conference on Computational Linguistics (NoDaLiDa).
  • Holur, P., Enevoldsen, K. C., Mboning, L., Georgiou, T., Bouchard, L.-S., Pellegrini, M., & Roychowdhury, V. (2025). Embed-Search-Align: DNA Sequence Alignment using Transformer Models. Bioinformatics, 41(3), btaf041.
  • Hansen, L., Bernstorff, M., Enevoldsen, K., Kolding, S., Damgaard, J. G., Perfalk, E., Nielbo, K. L., Danielsen, A. A., & Østergaard, S. D. (2025). Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning. JAMA Psychiatry, 82(5), 459–469. https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2830144
  • Bernstorff, M., Hansen, L., Enevoldsen, K., Damgaard, J., Hæstrup, F., Perfalk, E., Danielsen, A. A., & Østergaard, S. D. (2025). Development and validation of a machine learning model for prediction of type 2 diabetes in patients with mental illness. Acta Psychiatrica Scandinavica, 151(3), 245–258. https://doi.org/10.1111/acps.13687
  • Enevoldsen, K., Kardos, M., Muennighoff, N., & Nielbo, K. L. (2024). The Scandinavian embedding benchmarks: Comprehensive assessment of multilingual and monolingual text embedding. Advances in Neural Information Processing Systems (NeurIPS), 37, 40336–40358.
  • Enevoldsen, K. (2024). Augmenty: A Python Library for Structured Text Augmentation. Journal of Open Source Software, 9(96), 6370. https://doi.org/10.21105/joss.06370
  • Bernstorff, M., Vistisen, S. T., & Enevoldsen, K. C. (2024). Natural language processing for electronic health records in anaesthesiology: An introduction to clinicians with recommendations and pitfalls. Journal of Clinical Monitoring and Computing, 38(2), 241–245. https://doi.org/10.1007/s10877-024-01128-3
  • Lassen, I. M. S., Kristensen-McLachlan, R. D., Almasi, M., Enevoldsen, K., & Nielbo, K. L. (2024). Epistemic consequences of unfair tools. Digital Scholarship in the Humanities, 39(1), 198–214.
  • Charquero-Ballester, M., Walter, J. G., Rybner, A. S., Nissen, I. A., Enevoldsen, K. C., & Bechmann, A. (2024). Emotions on Twitter as crisis imprint in high-trust societies: Do ambient affiliations affect emotional expression during the pandemic? Plos One, 19(3), e0296801.
  • Enevoldsen, K., Jessen, E. T., & Baglini, R. (2024). DANSK and DaCy 2.6.0: Domain Generalization of Danish Named Entity Recognition. The Northern European Journal of Language Technology (NEJLT), 10, 14–29. https://nejlt.ep.liu.se/article/view/5249
  • Feldkamp, P., Lassche, A., Kostkan, J., Kardos, M., Enevoldsen, K., Baunvig, K., & Nielbo, K. (2024). Canonical Status and Literary Influence: A Comparative Study of Danish Novels from the Modern Breakthrough (1870–1900). In M. Hämäläinen, E. Öhman, S. Miyagawa, K. Alnajjar, & Y. Bizzoni (Eds.), Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities (pp. 140–155). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.nlp4dh-1.14
  • Nielbo, K. L., Enevoldsen, K., Baglini, R. B., Fano, E., Roepstorff, A., & Gao, J. (2023). Pandemic news information uncertainty—News dynamics mirror differential response strategies to COVID-19. PLOS ONE, 18(1), e0278098.
  • Hansen, L., Olsen, L. R., & Enevoldsen, K. (2023). TextDescriptives: A Python package for calculating a large variety of metrics from text. Journal of Open Source Software, 8(84), 5153. https://doi.org/10.21105/joss.05153
  • Bernstorff, M., Enevoldsen, K., Damgaard, J., Danielsen, A., & Hansen, L. (2023). timeseriesflattener: A Python package for summarizingfeatures from (medical) time series. Journal of Open Source Software, 8(83), 5197. https://doi.org/10.21105/joss.05197
  • Lassen, I. M. S., Almasi, M., Enevoldsen, K., & Kristensen-McLachlan, R. D. (2023). Detecting intersectionality in NER models: A data-driven approach. In S. Degaetano-Ortlieb, A. Kazantseva, N. Reiter, & S. Szpakowicz (Eds.), Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (pp. 116–127). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.latechclfl-1.13
  • Hansen, L., Enevoldsen, K., Bernstorff, M., Perfalk, E., Danielsen, A. A., Nielbo, K. L., & Østergaard, S. D. (2023). Lexical stability of psychiatric clinical notes from electronic health records over a decade. Acta Neuropsychiatrica, 1–11. https://doi.org/10.1017/neu.2023.46
  • Kolding, S., Nymann, K., Hansen, I., Enevoldsen, K., & Kristensen-McLachlan, R. (2023). DanSumT5: Automatic Abstractive Summarization for Danish. Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), 248–264. https://aclanthology.org/2023.nodalida-1.25
  • Nielbo, K. L., Hæstrup, F., Enevoldsen, K., Vahlstrup, P. B., Baglini, R. B., & Roepstorff, A. (2022). When no news is bad news: News-based change detection during COVID-19. DH Benelux Journal, 4, 107–120.
  • Enevoldsen, K. C., Danielsen, A. A., Rohde, C., Jefsen, O. H., Nielbo, K. L., & Østergaard, S. D. (2022). Monitoring of COVID-19 Pandemic-related Psychopathology using Machine Learning. Acta Neuropsychiatrica, 34(3), 148–152.
  • Kristensen-McLachlan, R. D., Lassen, I. M. S., Enevoldsen, K., Hansen, L., & Nielbo, K. L. (2022). Accuracy is not all you need. ADHO 2022-Tokyo. https://dh-abstracts.library.virginia.edu/works/11779
  • Waade, P. T., Enevoldsen, K. C., Vermillet, A.-Q., Simonsen, A., & Fusaroli, R. (2022). Introducing tomsup: Theory of mind simulations using Python. Behavior Research Methods, 55(5), 2197–2231.
  • Enevoldsen, K., Hansen, L., & Nielbo, K. L. (2021). DaCy: A unified framework for danish NLP. Ceur Workshop Proceedings, 2989, 206–216.
  • Have, I., & Enevoldsen, K. (2021). From close listening to distant listening: Developing tools for Speech-Music discrimination of Danish music radio. Digital Humanities Quarterly, 015(1).
  • Hansen, L., Enevoldsen, K. C., Bernstorff, M., Nielbo, K. L., Danielsen, A. A., & Østergaard, S. D. (2021). The PSYchiatric clinical outcome prediction (PSYCOP) cohort: Leveraging the potential of electronic health records in the treatment of mental disorders. Acta Neuropsychiatrica, 33(6), 323–330. https://doi.org/10.1017/neu.2021.22
  • Nielbo, K. L., Baglini, R. B., Vahlstrup, P. B., Enevoldsen, K. C., Bechmann, A., & Roepstorff, A. (2021, January). News information decoupling: An information signature of catastrophes in legacy news media. https://eadh2020-2021.org/
  • Baglini, R. B., Nielbo, K. L., Hæstrup, F., Enevoldsen, K., Vahlstrup, P. B., & Roepstorff, A. (2021, June 2). When no news is bad news: Detection of negative events from news media content. https://2021.dhbenelux.org/
  • Baglini, R. B., Hansen, L., Enevoldsen, K., & Nielbo, K. L. (2021). Multilingual Sentiment Normalization for Scandinavian Languages. Scandinavian Studies in Language, 12(1), 50–64.
  • Enevoldsen, K. C., & Hansen, L. (2017). Analysing political biases in danish newspapers using sentiment analysis. Journal of Language Works-Sprogvidenskabeligt Studentertidsskrift, 2(2), 87–98.

Preprints and Reports

  • Rystrøm, J. H., & Enevoldsen, K. C. (2024). Exposing Assumptions in AI Benchmarks through Cognitive Modelling (No. arXiv:2409.16849). arXiv. https://doi.org/10.48550/arXiv.2409.16849
  • Enevoldsen, K., Hansen, L., Nielsen, D. S., Egebæk, R. A. F., Holm, S. V., Nielsen, M. C., Bernstorff, M., Larsen, R., Jørgensen, P. B., Højmark-Bertelsen, M., Vahlstrup, P. B., Møldrup-Dalum, P., & Nielbo, K. (2023). Danish Foundation Models (No. arXiv:2311.07264). arXiv. https://doi.org/10.48550/arXiv.2311.07264
  • Charquero-Ballester, M., Walter, J. G., Sletten-Rybner, A., Nissen, I. A., Enevoldsen, K. C., & Bechmann, A. (2023). Emotional landscapes of misinformation spread. NORDIS – Nordic Observatory for Digital Media and Information Disorders.

Non-peer-reviewed software

  • Enevoldsen, K. (2021). Asent: Fast, flexible and transparent sentiment analysis.