12 citations · 15 across the 15 of their papers we have counts for
14 papers · 1 filter
Error-Type-Aware Loss Reweighting for Robust Named Entity Recognition with Noisy LLM Labels
Elena Merdjanovska, Jonas Golde, Alan Akbik
Large language models are increasingly used to annotate datasets for training smaller, task-specialized models such as named entity recognition. While this method yields effective…
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…
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…
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…
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…
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…
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Jonas Golde, Patrick Haller, Felix Hamborg +2
Most NLP tasks are modeled as supervised learning and thus require labeled training data to train effective models. However, manually producing such data at sufficient quality and…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
Jason Alan Fries, Leon Weber, Natasha Seelam +40
Training and evaluating language models increasingly requires the construction of meta-datasets --diverse collections of curated data with clear provenance. Natural language prompt…