Comparative Analysis of Zero-shot, Few-shot, and One-shot Learning in Natural Language Processing

Recent advancements in Natural Language Processing (NLP) [1] have been significantly influenced by various learning paradigms, notably Zero-shot, Few-shot, and One-shot learning. This paper presents a comprehensive comparative analysis of these paradigms, detailing their methodologies, performance metrics, and applicability to diverse NLP tasks. Zero-shot learning models, such as GPT-4 and Flan-T5, demonstrate robust performance without task-specific training but require substantial computational resources. Few-shot learning, leveraging models like GPT-3.5-turbo and Flan-T5, balances efficiency and accuracy, achieving near state-of-the-art performance with minimal data. One-shot learning, exemplified by Siamese Networks, shows promise in highly constrained data environments but generally underperforms in complex NLP tasks compared to few-shot learning. Through experimental evaluations on benchmark datasets, this paper highlights the strengths and limitations of each approach, providing insights into their practical implications and future research directions