Challenging Specialized Transformers on Zero-Shot Classification

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.