ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification

Yaxin Zhu; Hamed Zamani

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arXiv · 2311.09649

ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification

Yaxin Zhu·Hamed Zamani

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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 Multilabel Learning (ICXML), a two-stage framework that cuts down the search space by generating a set of candidate labels through incontext 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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Yaxin Zhu, Hamed Zamani. 2023-11-16. ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification. https://arxiv.org/abs/2311.09649

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