38 citations · 49 across the 20 of their papers we have counts for
23 papers
Verbalizing Activation Differences with Natural Language Autoencoders
Andrea Blasi Núñez, Gianluca Barmina, Federico Torrielli +3
Activation verbalizers translate the internal representations of large language models into human-readable text. The recently proposed natural language autoencoders (NLAs) learn th…
Selecting The Most Informative Tokens in Natural Language Autoencoders
Federico Torrielli, Gianluca Barmina, Andrea Blasi Núñez +4
Natural language autoencoders translate a language model's internal activations into readable explanations. Explaining every token position is costly. Which positions should an aud…
Write Once, Run Everywhere: The Axon DSL for Shape-Safe and Framework-Agnostic LLM Architectures
Jacob Nielsen, Danial Namazifard, Lukas Galke Poech +1
The entire ecosystem of open-source language models effectively relies on a single platform. What if this platform was forced to shut down tomorrow? Implementing and maintaining ef…
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…
The Arbiter Agent: Continually Monitoring Multi-Agent Conversations to Detect Emergent Misalignment
Filippo Tonini, Federico Torrielli, Anton Danholt Lautrup +3
As AI systems built from multiple language-model agents become more common, they are increasingly used to make decisions together: discussing, negotiating, and acting on shared tas…
BrainSurgery: Reproducible and Reliable Declarative Weight Manipulations for Model Editing and Upcycling
Gianluca Barmina, Annemette Broch Pirchert, Andrea Blasi Núñez +2
As deep learning models scale, managing, inspecting, and modifying large checkpoints has become increasingly challenging. Researchers often need to alter model weights for layer re…
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…
LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs
Gianluca Barmina, Peter Schneider-Kamp, Lukas Galke Poech
Large language models can reproduce training data, but existing memorization evaluations mostly measure whether models can be forced to do so, rather than whether they do so under…
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…
The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment
William Brach, Federico Torrielli, Stine Lyngsø Beltoft +3
Moltbook is a Reddit-like platform where OpenClaw agents post, comment, and vote at scale - a so far unprecedented incident that comes with serious safety concerns. With the aim of…
Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals
Federico Torrielli, Peter Schneider-Kamp, Lukas Galke Poech
An activation oracle is a language model trained to convert the activations of another model into natural language. However, it is so far unclear to what extent these verbalization…
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…
FlexMoRE: A Flexible Mixture of Rank-heterogeneous Experts for Efficient Federatedly-trained Large Language Models
Annemette Brok Pirchert, Jacob Nielsen, Mogens Henrik From +2
Recent advances in mixture-of-experts architectures have shown that individual experts models can be trained federatedly, i.e., in isolation from other experts by using a common ba…
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,…
DaLA: Danish Linguistic Acceptability Evaluation Guided by Real World Errors
Gianluca Barmina, Nathalie Carmen Hau Norman, Peter Schneider-Kamp +1
We present an enhanced benchmark for evaluating linguistic acceptability in Danish. We first analyze the most common errors found in written Danish. Based on this analysis, we intr…
Training Language Models to Use Prolog as a Tool
Niklas Mellgren, Peter Schneider-Kamp, Lukas Galke Poech
Language models frequently produce plausible yet incorrect reasoning traces that are difficult to verify. We investigate fine-tuning models to use Prolog as an external symbolic re…
Achieving Hilbert-Schmidt Independence Under Rényi Differential Privacy for Fair and Private Data Generation
Tobias Hyrup, Emmanouil Panagiotou, Arjun Roy +3
As privacy regulations such as the GDPR and HIPAA and responsibility frameworks for artificial intelligence such as the AI Act gain traction, the ethical and responsible use of rea…
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…
Randomized PCA Forest for Unsupervised Outlier Detection
Muhammad Rajabinasab, Farhad Pakdaman, Moncef Gabbouj +2
We propose a novel unsupervised outlier detection method based on Randomized Principal Component Analysis (PCA). Motivated by the performance of Randomized PCA (RPCA) Forest in app…
Disjoint Generation of Synthetic Data
Anton Danholt Lautrup, Muhammad Rajabinasab, Tobias Hyrup +2
We propose a new framework for generating tabular synthetic datasets via disjoint generative models. In this paradigm, a dataset is partitioned into disjoint subsets that are suppl…
Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU Tasks
Dan Saattrup Nielsen, Kenneth Enevoldsen, Peter Schneider-Kamp
This paper explores the performance of encoder and decoder language models on multilingual Natural Language Understanding (NLU) tasks, with a broad focus on Germanic languages. Bui…
SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data
Anton Danholt Lautrup, Tobias Hyrup, Arthur Zimek +1
With the growing demand for synthetic data to address contemporary issues in machine learning, such as data scarcity, data fairness, and data privacy, having robust tools for asses…
Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data
Tobias Hyrup, Anton Danholt Lautrup, Arthur Zimek +1
Data sharing is a necessity for innovative progress in many domains, especially in healthcare. However, the ability to share data is hindered by regulations protecting the privacy…