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

arXiv:2311.09649v2 Announce Type: replace
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 …

abstract arxiv classification context cs.cl cs.lg framework highlighting in-context learning instance labels multiple paper research space supervision type world zero-shot