  For extreme multi-label classification (XMC), existing classification-based
models poorly perform for tail labels and often ignore the semantic relations
among labels, like treating "Wikipedia" and "Wiki" as independent and separate
labels. In this paper, we cast XMC as a generation task (XLGen), where we
benefit from pre-trained text-to-text models. However, generating labels from
the extremely large label space is challenging without any constraints or
guidance. We, therefore, propose to guide label generation using label cluster
information to hierarchically generate lower-level labels. We also find that
frequency-based label ordering and using decoding ensemble methods are critical
factors for the improvements in XLGen. XLGen with cluster guidance
significantly outperforms the classification and generation baselines on tail
labels, and also generally improves the overall performance in four popular XMC
benchmarks. In human evaluation, we also find XLGen generates unseen but
plausible labels. Our code is now available at
https://github.com/alexa/xlgen-eacl-2023.
