Zero-Shot Learning for Next-Gen Language Understanding Tasks

Abstract

Zero-shot learning (ZSL) has emerged as a powerful paradigm enabling models to generalize to unseen classes or tasks without requiring task-specific labeled data. In next-generation language understanding, where the diversity and volume of linguistic phenomena continually outpace the availability of annotated corpora, ZSL offers a promising route to scalability and adaptability. This manuscript investigates the application of ZSL to a range of advanced natural language understanding tasks—including semantic role labeling, coreference resolution, and commonsense inference—by leveraging rich semantic embeddings and prompt-based task descriptions. We conduct a large-scale empirical evaluation across five benchmark datasets, comparing multiple embedding spaces (e.g., BERT, RoBERTa, and GPT-derived representations) and prompt-engineering strategies. Our statistical analysis (see Table 1) examines performance gains in precision, recall, and F₁ scores, demonstrating that task-specific prompt design coupled with high-dimensional semantic priors yields significant improvements (up to +12 F₁) over naive prompt baselines. We further analyze error distributions to identify persistent challenges—such as disambiguation in low-resource domains and subtle pragmatic reasoning. Our methodology section details dataset selection, embedding construction, prompt formulation, and evaluation protocols. The results section presents both quantitative and qualitative insights, highlighting best practices for deploying ZSL in production-grade language systems. We conclude by summarizing key findings and outlining a forward-looking research agenda, including adaptive prompt learning and multi-modal zero-shot frameworks.