@inproceedings{zhu-zamani-2024-icxml,
title = "{ICXML}: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification",
author = "Zhu, Yaxin and
Zamani, Hamed",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.134",
pages = "2086--2098",
abstract = "This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to predict multiple labels for each instance from an extremely large label space. While existing research has primarily focused on fully supervised XMC, real-world scenarios often lack supervision signals, highlighting the importance of zero-shot settings. Given the large label space, utilizing in-context learning approaches is not trivial. We address this issue by introducing In-Context Extreme Multi-label Learning (ICXML), a two-stage framework that cuts down the search space by generating a set of candidate labels through in-context learning and then reranks them. Extensive experiments suggest that ICXML advances the state of the art on two diverse public benchmarks.",
}
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%0 Conference Proceedings
%T ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification
%A Zhu, Yaxin
%A Zamani, Hamed
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F zhu-zamani-2024-icxml
%X This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to predict multiple labels for each instance from an extremely large label space. While existing research has primarily focused on fully supervised XMC, real-world scenarios often lack supervision signals, highlighting the importance of zero-shot settings. Given the large label space, utilizing in-context learning approaches is not trivial. We address this issue by introducing In-Context Extreme Multi-label Learning (ICXML), a two-stage framework that cuts down the search space by generating a set of candidate labels through in-context learning and then reranks them. Extensive experiments suggest that ICXML advances the state of the art on two diverse public benchmarks.
%U https://aclanthology.org/2024.findings-naacl.134
%P 2086-2098
Markdown (Informal)
[ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification](https://aclanthology.org/2024.findings-naacl.134) (Zhu & Zamani, Findings 2024)
ACL