@inproceedings{11568_1218007,
 abstract = {This paper investigates the feasibility of employing basic prompting systems for domain-specific language models. The study focuses on bureaucratic language and uses the recently introduced BureauBERTo model for experimentation. The experiments reveal that while further pre-trained models exhibit reduced robustness concerning general knowledge, they display greater adaptability in modeling domain-specific tasks, even under a zero-shot paradigm. This demonstrates the potential of leveraging simple prompting systems in specialized contexts, providing valuable insights both for research and industry.},
 address = {Aachen},
 author = {Auriemma, S. and Madeddu, M. and Miliani, M. and Bondielli, A. and Lenci, A. and Passaro, L.},
 booktitle = {Proceedings of the 9th Italian Conference on Computational Linguistics},
 isbn = {979-12-5500-084-6},
 keywords = {Domain Adaptation,Italian Bureaucratic Language,Prompting,Public Administration,Transformers,Zero-shot},
 publisher = {CEUR-WS},
 title = {Challenging Specialized Transformers on Zero-Shot Classification},
 volume = {3596},
 year = {2023}
}