# Cluster-Guided Label Generation in Extreme Multi-Label Classification

arXiv: https://arxiv.org/abs/2302.09150 · HTML: https://mlsanity.com/paper/2302.09150/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 21 of 21)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks](https://mlsanity.com/paper/1810.00825/) (2018)
- [Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://mlsanity.com/paper/2109.10686/) (2021)
- [AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification](https://mlsanity.com/paper/1811.01727/) (2018)
- [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018)
- [DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification](https://mlsanity.com/paper/1609.02521/) (2016)
- [Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2110.00685/) (2021)
- [Large-scale Multi-label Learning with Missing Labels](https://mlsanity.com/paper/1307.5101/) (2013)
- [A no-regret generalization of hierarchical softmax to extreme multi-label classification](https://mlsanity.com/paper/1810.11671/) (2018)
- [PECOS: Prediction for Enormous and Correlated Output Spaces](https://mlsanity.com/paper/2010.05878/) (2020)
- [Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2007.02439/) (2020)
- [Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning](https://mlsanity.com/paper/2106.03103/) (2021)
- [Extreme Zero-Shot Learning for Extreme Text Classification](https://mlsanity.com/paper/2112.08652/) (2021)
- [Open Vocabulary Extreme Classification Using Generative Models](https://mlsanity.com/paper/2205.05812/) (2022)
- [Order-free Learning Alleviating Exposure Bias in Multi-label Classification](https://mlsanity.com/paper/1909.03434/) (2019)
- [Pseudo Labeling and Negative Feedback Learning for Large-scale Multi-label Domain Classification](https://mlsanity.com/paper/2003.03728/) (2020)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Multi-Label Generation with Probabilistic Level-Constraint](https://mlsanity.com/paper/2505.03775/) (2025)

## Abstract (arXiv)

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.