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Mimir: A Sub-2B Hierarchical Reasoning Model Trained on Permissible Data Punches Above Its Weight Class
Peter Schneider-Kamp, Jacob Nielsen, Gianluca Barmina +4
Training capable language models typically relies on web-scale pretraining followed by multi-stage post-training, creating substantial computational and data-governance barriers fo…
PsychoSafe: Eliciting Psychologically-Informed Refusals in Large Language Models
Gianluca Barmina, Federico Torrielli, Sven Harms +7
Large language models (LLMs) routinely face requests that should be refused, creating a trade-off between helpfulness and harm prevention. However, refusals themselves can be helpf…
Emergent Languages in Populations of Language Model Agents: From Token Efficiency to Oversight Evasion
Stine Lyngsø Beltoft, William Brach, Federico Torrielli +5
Monitoring autonomous language model agents currently relies mostly on surface behavior. But what happens when agent populations invent new languages with the goal of avoiding huma…
SommBench: Assessing Sommelier Expertise of Language Models
William Brach, Tomas Bedej, Jacob Nielsen +10
With the rapid advances of large language models, it becomes increasingly important to systematically evaluate their multilingual and multicultural capabilities. Previous cultural…
SDUs DAISY: A Benchmark for Danish Culture
Jacob Nielsen, Stine L. Beltoft, Peter Schneider-Kamp +1
We introduce Daisy, a factual knowledge benchmark for Danish cultural heritage, based on curated topics from the Danish Culture Canon 2006. For each artifact in the culture canon,…
Dynaword: From One-shot to Continuously Developed Datasets
Kenneth Enevoldsen, Kristian Nørgaard Jensen, Jan Kostkan +14
Large-scale datasets are foundational for research and development in natural language processing. However, current approaches face three key challenges: (1) reliance on ambiguousl…