2 citations · 4 across the 3 of their papers we have counts for
3 papers · 1 filter
PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning
Jonas Rieger, Mattes Ruckdeschel, Gregor Wiedemann
Few-shot learning and parameter-efficient fine-tuning (PEFT) are crucial to overcome the challenges of data scarcity and ever growing language model sizes. This applies in particul…
Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine
Jonas Rieger, Kostiantyn Yanchenko, Mattes Ruckdeschel +3
Pre-trained language models (PLM) based on transformer neural networks developed in the field of natural language processing (NLP) offer great opportunities to improve automatic co…
Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debates
Aida Kostikova, Benjamin Paassen, Dominik Beese +3
Solidarity is a crucial concept to understand social relations in societies. In this paper, we explore fine-grained solidarity frames to study solidarity towards women and migrants…