Instruction Tuning with Lexicons for Zero-Shot Style Classification
Ruohao Guo Wei Xu Alan Ritter
Style is used to convey authors' intentions and attitudes. Despite the
success of large pre-trained language models on style classification, prior
work relies on fine-tuning with labeled examples. Prompting large language
models to classify style without fine-tuning is challenging because language
styles can be difficult to define. In this study, we investigate the
effectiveness of style lexicons as a means for instructing language models how
to identify new styles that are unseen during training. Our experiments show
that lexicon-based instructions improve transfer zero-shot performance
significantly. We will release our code and data.