@inproceedings{xu-etal-2024-x,
title = "{X}-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification",
author = "Xu, Hanzi and
Chen, Muhao and
Huang, Lifu and
Vucetic, Slobodan and
Yin, Wenpeng",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.276",
pages = "4652--4665",
abstract = "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** (**B**inary **IN**ference **B**ased on **IN**struction 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.",
}
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<abstract>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** (**B**inary **IN**ference **B**ased on **IN**struction 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.</abstract>
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%0 Conference Proceedings
%T X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification
%A Xu, Hanzi
%A Chen, Muhao
%A Huang, Lifu
%A Vucetic, Slobodan
%A Yin, Wenpeng
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F xu-etal-2024-x
%X 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** (**B**inary **IN**ference **B**ased on **IN**struction 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.
%U https://aclanthology.org/2024.findings-acl.276
%P 4652-4665
Markdown (Informal)
[X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification](https://aclanthology.org/2024.findings-acl.276) (Xu et al., Findings 2024)
ACL