9 papers
FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition
Jonas Golde, Patrick Haller, Alan Akbik
Recent multilingual named entity recognition (NER) work has shown that large language models (LLMs) can provide effective synthetic supervision, yet such datasets have mostly appea…
PISA-Bench: The PISA Index as a Multilingual and Multimodal Metric for the Evaluation of Vision-Language Models
Patrick Haller, Fabio Barth, Jonas Golde +2
Vision-language models (VLMs) have demonstrated remarkable progress in multimodal reasoning. However, existing benchmarks remain limited in terms of high-quality, human-verified ex…
Sample-Efficient Language Modeling with Linear Attention and Lightweight Enhancements
Patrick Haller, Jonas Golde, Alan Akbik
We study architectural and optimization techniques for sample-efficient language modeling under the constraints of the BabyLM 2025 shared task. Our model, BLaLM, replaces self-atte…
LLM Knowledge is Brittle: Truthfulness Representations Rely on Superficial Resemblance
Patrick Haller, Mark Ibrahim, Polina Kirichenko +2
For Large Language Models (LLMs) to be reliable, they must learn robust knowledge that can be generally applied in diverse settings -- often unlike those seen during training. Yet,…
From Data to Knowledge: Evaluating How Efficiently Language Models Learn Facts
Daniel Christoph, Max Ploner, Patrick Haller +1
Sample efficiency is a crucial property of language models with practical implications for training efficiency. In real-world text, information follows a long-tailed distribution.…
What Matters in Linearizing Language Models? A Comparative Study of Architecture, Scale, and Task Adaptation
Patrick Haller, Jonas Golde, Alan Akbik
Linearization has emerged as a strategy for developing efficient language models (LMs). Starting from an existing Transformer-based LM, linearization replaces the attention compone…