Instruction Tuning with Lexicons for Zero-Shot Style Classification
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.
PDF AbstractTasks
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
