10 papers · 1 filter
Repetition over Diversity: High-Signal Data Filtering for Sample-Efficient German Language Modeling
Ansar Aynetdinov, Patrick Haller, Alan Akbik
Recent research has shown that filtering massive English web corpora into high-quality subsets significantly improves training efficiency. However, for high-resource non-English la…
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…
What Matters When Building Universal Multilingual Named Entity Recognition Models?
Jonas Golde, Patrick Haller, Alan Akbik
Recent progress in universal multilingual named entity recognition (NER) has been driven by multilingual transformer models, task-specific architectures, custom loss functions, and…
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…
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,…