ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification
Yaxin Zhu, Hamed Zamani · 2024
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.