1 citations · 1 across the 19 of their papers we have counts for
20 papers
Social Bias or Social Desirability? Comparing Factorial Survey Experiments for Humans and Large Language Models
Franziska Weeber, Thomas Hinz, Konstantin Mozer +3
Factorial survey experiments are a survey method from the social sciences that uses descriptions of socially complex situation (``vignettes'') to elicit judgments such as social bi…
news-crawler-LM: A Small Long-Context Model For High-Quality News Crawling
Pascal Stolzenburg, Jonas Golde, Max Dallabetta +1
Extracting structured content from news pages remains challenging due to heterogeneous HTML layouts, inconsistent markup, and substantial boilerplate such as navigation elements an…
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…
Beyond Marginal Distributions: A Framework to Evaluate the Representativeness of Demographic-Aligned LLMs
Tristan Williams, Franziska Weeber, Sebastian Padó +1
Large language models are increasingly used to represent human opinions, values, or beliefs, and their steerability towards these ideals is an active area of research. Existing wor…
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…
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…
Towards a Principled Evaluation of Knowledge Editors
Sebastian Pohl, Max Ploner, Alan Akbik
Model editing has been gaining increasing attention over the past few years. For Knowledge Editing in particular, more challenging evaluation datasets have recently been released.…
Question Decomposition for Retrieval-Augmented Generation
Paul J. L. Ammann, Jonas Golde, Alan Akbik
Grounding large language models (LLMs) in verifiable external sources is a well-established strategy for generating reliable answers. Retrieval-augmented generation (RAG) is one su…
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…
MastermindEval: A Simple But Scalable Reasoning Benchmark
Jonas Golde, Patrick Haller, Fabio Barth +1
Recent advancements in large language models (LLMs) have led to remarkable performance across a wide range of language understanding and mathematical tasks. As a result, increasing…
BabyHGRN: Exploring RNNs for Sample-Efficient Training of Language Models
Patrick Haller, Jonas Golde, Alan Akbik
This paper explores the potential of recurrent neural networks (RNNs) and other subquadratic architectures as competitive alternatives to transformer-based models in low-resource l…
Familiarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data
Jonas Golde, Patrick Haller, Max Ploner +3
Zero-shot named entity recognition (NER) is the task of detecting named entities of specific types (such as 'Person' or 'Medicine') without any training examples. Current research…
TransformerRanker: A Tool for Efficiently Finding the Best-Suited Language Models for Downstream Classification Tasks
Lukas Garbas, Max Ploner, Alan Akbik
Classification tasks in NLP are typically addressed by selecting a pre-trained language model (PLM) from a model hub, and fine-tuning it for the task at hand. However, given the ve…
LM-PUB-QUIZ: A Comprehensive Framework for Zero-Shot Evaluation of Relational Knowledge in Language Models
Max Ploner, Jacek Wiland, Sebastian Pohl +1
Knowledge probing evaluates the extent to which a language model (LM) has acquired relational knowledge during its pre-training phase. It provides a cost-effective means of compari…
PECC: Problem Extraction and Coding Challenges
Patrick Haller, Jonas Golde, Alan Akbik
Recent advancements in large language models (LLMs) have showcased their exceptional abilities across various tasks, such as code generation, problem-solving and reasoning. Existin…
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition
Jonas Golde, Felix Hamborg, Alan Akbik
Few-shot named entity recognition (NER) detects named entities within text using only a few annotated examples. One promising line of research is to leverage natural language descr…
Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions
Max Dallabetta, Conrad Dobberstein, Adrian Breiding +1
This paper introduces Fundus, a user-friendly news scraper that enables users to obtain millions of high-quality news articles with just a few lines of code. Unlike existing news s…