X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot
  Learning Simultaneously in Classification
Hanzi Xu Muhao Chen Lifu Huang Slobodan Vucetic Wenpeng Yin
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 co-occur
without predefined limits. Here, X can span from 0 to positive infinity. 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-of-the-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.