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cs.CL2024
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…
cs.CL2023
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…