  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.
