OdenseNLP at LREC26
OdenseNLP was at LREC 2026 and authored/contributed in three papers:
OdenseNLP
OdenseNLP
Safe, Efficient and Open Natural Language Processing @ University of Southern Denmark
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MIST at OdenseNLP
Assistant Professor Lukas Galke Poech from OdenseNLP is leading MIST: Scalable Mechanistic Interpretability for Safe and Trustworthy LLM Agents, a project focused on making language ...
OdenseNLP is part of the Danish Foundation Models initiative.
OdenseNLP is part of the Danish Foundation Models (DFM) initiative: a national collaboration developing, evaluating, and adapting open language AI for Danish society. DFM focuses on ...
Current focuses
Low-resource NLP
Language technologies for low-resource langauges, particularly Danish and neighboring Scandinavian languages.
Efficient NLP
Fast and efficient NLP architectures and methods.
AI Safety & Interpretability
Making AI systems more safe, trustworthy, and interpretable.
Repositories summary
See all repositoriesPsychoSafe
Pipeline for building and evaluating psychologically informed refusal behavior in large language models through data creation, prompting, fine-tuni...
PropMe
Framework for evaluating memorization and propensity-aware memorization of training data in large language models.
DaLA
Source code for creating the Danish Corpus of Linguistic Acceptability, designed to evaluate Danish linguistic acceptability with real-world errors.
Datasets summary
See all datasetsDaLA
Danish linguistic acceptability dataset with corrupted and non-corrupted sentences, published as ~8.68k examples with train/val/test and full-train...
SDU-Daisy
Danish-culture benchmark dataset based on the Danish Culture Canon, with 746 closed question-answer pairs for evaluating LLM cultural understanding.