X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification
Hanzi Xu, Muhao Chen, Lifu Huang, Slobodan Vučetić, Wenpeng Yin · 2024
In recent years, few-shot and zero-shot learning, which learn to predict labels with limited annotated instances, have garnered significant attention.Traditional approaches often treat frequent-shot (freq-shot; labels with abundant instances), few-shot, and zero-shot learning as distinct challenges, optimizing systems for just one of these scenarios.Yet, in real-world settings, label occurrences vary greatly.Some of them might appear thousands of times, while others might only appear sporadically or not at all.For practical deployment, it is crucial that a system can adapt to any label occurrence.We introduce a novel classification challenge: X-Shot, reflecting a real-world context where freq-shot, few-shot, and zero-shot labels cooccur without predefined limits.Here, X can span from 0 to +∞.The crux of X-Shot centers on open-domain generalization and devising a system versatile enough to manage various label scenarios.To solve X-Shot, we propose BinBin (binary inference based on instruction following) that leverages the Indirect Supervision from a large collection of NLP tasks via instruction following, bolstered by Weak Supervision provided by large language models.BinBin surpasses previous state-ofthe-art techniques on three benchmark datasets across multiple domains.To our knowledge, this is the first work addressing X-Shot learning, where X remains variable.1