Exploring Zero-Shot Learning in Natural Language Processing for Cross-Domain Applications

The research assesses how three and two state-of-the-art pre-trained language models (PLMs) function alongside BERT and BioBERT while operating on various natural language processing (NLP) functions which incorporate sentiment analysis, text classification, entity recognition and cross-domain sentiment classification. BERT and BioBERT achieve superior performance than all other models by delivering optimal accuracy ratings together with high precision and recall while obtaining the highest F1-score across all tasks. The generalization ability of BERT enables it to maintain top performance across four different domains which include product reviews, movie reviews, social media content and news articles. BioBERT demonstrates particular effectiveness when handling biomedical Natural Language Processing applications. The model performance of T5 matches BERT although BERT demonstrates better outcomes in specific tasks. RoBERTa together with GPT-2 achieves moderate success in particular use cases but consistently performs below the current best NLP models. BERT-based models demonstrate superior performance in various NLP applications which demonstrates their strength in dealing with complicated operations yet researchers need to investigate RoBERTa and GPT-2 specifically within individual domains to achieve superior outcomes.