mlsanityData to 14 Oct 2026

LH-Mix: Local Hierarchy Correlation Guided Mixup over Hierarchical Prompt Tuning

arXiv 2412.16963 ↗First posted 22 Dec 2024cs.CLcited by 1

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Abstract (arXiv)

Hierarchical text classification (HTC) aims to assign one or more labels in the hierarchy for each text. Many methods represent this structure as a global hierarchy, leading to redundant graph structures. To address this, incorporating a text-specific local hierarchy is essential. However, existing approaches often model this local hierarchy as a sequence, focusing on explicit parent-child relationships while ignoring implicit correlations among sibling/peer relationships. In this paper, we first integrate local hierarchies into a manual depth-level prompt to capture parent-child relationships. We then apply Mixup to this hierarchical prompt tuning scheme to improve the latent correlation within sibling/peer relationships. Notably, we propose a novel Mixup ratio guided by local hierarchy correlation to effectively capture intrinsic correlations. This Local Hierarchy Mixup (LH-Mix) model demonstrates remarkable performance across three widely-used datasets.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 45 references that are in the map. First posted on arXiv 22 Dec 2024.

Builds on

27 of 45 references.

References · 27

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
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  6. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  7. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  8. 2020MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classificationcited by 187
  9. 2018MixUp as Locally Linear Out-Of-Manifold Regularizationcited by 158
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15 more
  1. 2020Dual Mixup Regularized Learning for Adversarial Domain Adaptationcited by 99
  2. 2022Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classificationcited by 52
  3. 2020Combining Ensembles and Data Augmentation can Harm your Calibrationcited by 44
  4. 2019Hierarchical Text Classification with Reinforced Label Assignmentcited by 41
  5. 2019Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classificationcited by 35
  6. 2019Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classificationcited by 32
  7. 2022HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classificationcited by 31
  8. 2019Hyperbolic Interaction Model For Hierarchical Multi-Label Classificationcited by 30
  9. 2021HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximizationcited by 25
  10. 2019Virtual Mixup Training for Unsupervised Domain Adaptationcited by 21
  11. 2023HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classificationcited by 17
  12. 2022Exploiting Global and Local Hierarchies for Hierarchical Text Classificationcited by 15
  13. 2022Preventing Manifold Intrusion with Locality: Local Mixupcited by 11
  14. 2021Hierarchical Text Classification As Sub-Hierarchy Sequence Generationcited by 6
  15. 2023Single-channel speech enhancement using learnable loss mixupcited by 2

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Taxon: Hierarchical Tax Code Prediction with Semantically Aligned LLM Expert Guidancecited by 0