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

arXiv: https://arxiv.org/abs/2412.16963 · HTML: https://mlsanity.com/paper/2412.16963/

## Key facts

- Cited by 1 papers 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 (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification](https://mlsanity.com/paper/2004.12239/) (2020)
- [MixUp as Locally Linear Out-Of-Manifold Regularization](https://mlsanity.com/paper/1809.02499/) (2018)
- [Remix: Rebalanced Mixup](https://mlsanity.com/paper/2007.03943/) (2020)
- [Augmenting Data with Mixup for Sentence Classification: An Empirical Study](https://mlsanity.com/paper/1905.08941/) (2019)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://mlsanity.com/paper/1709.08267/) (2017)
- [Dual Mixup Regularized Learning for Adversarial Domain Adaptation](https://mlsanity.com/paper/2007.03141/) (2020)
- [Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification](https://mlsanity.com/paper/2203.03825/) (2022)
- [Combining Ensembles and Data Augmentation can Harm your Calibration](https://mlsanity.com/paper/2010.09875/) (2020)
- [Hierarchical Text Classification with Reinforced Label Assignment](https://mlsanity.com/paper/1908.10419/) (2019)
- [Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification](https://mlsanity.com/paper/1909.04176/) (2019)
- [Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification](https://mlsanity.com/paper/1906.04898/) (2019)
- [HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification](https://mlsanity.com/paper/2204.13413/) (2022)
- [Hyperbolic Interaction Model For Hierarchical Multi-Label Classification](https://mlsanity.com/paper/1905.10802/) (2019)
- [HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization](https://mlsanity.com/paper/2104.05220/) (2021)
- [Virtual Mixup Training for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1905.04215/) (2019)
- [HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification](https://mlsanity.com/paper/2305.15182/) (2023)
- [Exploiting Global and Local Hierarchies for Hierarchical Text Classification](https://mlsanity.com/paper/2205.02613/) (2022)
- [Preventing Manifold Intrusion with Locality: Local Mixup](https://mlsanity.com/paper/2201.04368/) (2022)
- [Hierarchical Text Classification As Sub-Hierarchy Sequence Generation](https://mlsanity.com/paper/2111.11104/) (2021)
- [Single-channel speech enhancement using learnable loss mixup](https://mlsanity.com/paper/2312.17255/) (2023)

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

- [Taxon: Hierarchical Tax Code Prediction with Semantically Aligned LLM Expert Guidance](https://mlsanity.com/paper/2601.08418/) (2026)

## 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.