# HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification

arXiv: https://arxiv.org/abs/2204.13413 · HTML: https://mlsanity.com/paper/2204.13413/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [Learning How to Ask: Querying LMs with Mixtures of Soft Prompts](https://mlsanity.com/paper/2104.06599/) (2021)
- [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://mlsanity.com/paper/2010.15980/) (2020)
- [WARP: Word-level Adversarial ReProgramming](https://mlsanity.com/paper/2101.00121/) (2021)
- [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://mlsanity.com/paper/2104.07650/) (2021)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://mlsanity.com/paper/1709.08267/) (2017)
- [Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification](https://mlsanity.com/paper/2203.03825/) (2022)
- [Hierarchical Text Classification with Reinforced Label Assignment](https://mlsanity.com/paper/1908.10419/) (2019)
- [Effective Use of Word Order for Text Categorization with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1058/) (2014)
- [Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification](https://mlsanity.com/paper/1909.04176/) (2019)
- [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)
- [LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Network](https://mlsanity.com/paper/2009.10938/) (2020)

## Built on by (most cited first, 30 of 31)

- [Making Large Language Models Better Reasoners with Alignment](https://mlsanity.com/paper/2309.02144/) (2023)
- [DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2305.03973/) (2023)
- [HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification](https://mlsanity.com/paper/2305.15182/) (2023)
- [Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classification](https://mlsanity.com/paper/2406.17534/) (2024)
- [Propulsion: Steering LLM with Tiny Fine-Tuning](https://mlsanity.com/paper/2409.10927/) (2024)
- [DETree: DEtecting Human-AI Collaborative Texts via Tree-Structured Hierarchical Representation Learning](https://mlsanity.com/paper/2510.17489/) (2025)
- [Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification](https://mlsanity.com/paper/2305.16885/) (2023)
- [A Simple and Effective Framework for Strict Zero-Shot Hierarchical Classification](https://mlsanity.com/paper/2305.15282/) (2023)
- [Revisiting Hierarchical Text Classification: Inference and Metrics](https://mlsanity.com/paper/2410.01305/) (2024)
- [Recent Advances in Hierarchical Multi-label Text Classification: A Survey](https://mlsanity.com/paper/2307.16265/) (2023)
- [HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification](https://mlsanity.com/paper/2402.01696/) (2024)
- [DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade](https://mlsanity.com/paper/2305.14751/) (2023)
- [Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification](https://mlsanity.com/paper/2310.05128/) (2023)
- [Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding](https://mlsanity.com/paper/2310.11721/) (2023)
- [Leveraging Label Semantics and Meta-Label Refinement for Multi-Label Question Classification](https://mlsanity.com/paper/2411.01841/) (2024)
- [Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification](https://mlsanity.com/paper/2402.18825/) (2024)
- [Hierarchical Query Classification in E-commerce Search](https://mlsanity.com/paper/2403.06021/) (2024)
- [Domain-Hierarchy Adaptation via Chain of Iterative Reasoning for Few-shot Hierarchical Text Classification](https://mlsanity.com/paper/2407.08959/) (2024)
- [Your Extreme Multi-label Classifier is Secretly a Hierarchical Text Classifier for Free](https://mlsanity.com/paper/2411.13687/) (2024)
- [Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification](https://mlsanity.com/paper/2311.16650/) (2023)
- [Labels Need Prompts Too: Mask Matching for Natural Language Understanding Tasks](https://mlsanity.com/paper/2312.08726/) (2023)
- [Introducing Three New Benchmark Datasets for Hierarchical Text Classification](https://mlsanity.com/paper/2411.19119/) (2024)
- [LH-Mix: Local Hierarchy Correlation Guided Mixup over Hierarchical Prompt Tuning](https://mlsanity.com/paper/2412.16963/) (2024)
- [Hierarchical Text Classification Using Black Box Large Language Models](https://mlsanity.com/paper/2508.04219/) (2025)
- [EPPC-OASIS: Ontology-Aware Adaptation and Structured Inference Refinement for Electronic Patient-Provider Communication Mining in Secure Messages](https://mlsanity.com/paper/2605.24172/) (2026)
- [Making Language Model a Hierarchical Classifier](https://mlsanity.com/paper/2507.12930/) (2025)
- [Combining Language and Topic Models for Hierarchical Text Classification](https://mlsanity.com/paper/2507.16490/) (2025)
- [Taxon: Hierarchical Tax Code Prediction with Semantically Aligned LLM Expert Guidance](https://mlsanity.com/paper/2601.08418/) (2026)
- [Mastering the Minority: An Uncertainty-guided Multi-Expert Framework for Challenging-tailed Sequence Learning](https://mlsanity.com/paper/2603.15708/) (2026)
- [SCHK-HTC: Sibling Contrastive Learning with Hierarchical Knowledge-Aware Prompt Tuning for Hierarchical Text Classification](https://mlsanity.com/paper/2604.15998/) (2026)

## Abstract (arXiv)

Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex label hierarchy. Recently, the pretrained language models (PLM)have been widely adopted in HTC through a fine-tuning paradigm. However, in this paradigm, there exists a huge gap between the classification tasks with sophisticated label hierarchy and the masked language model (MLM) pretraining tasks of PLMs and thus the potentials of PLMs can not be fully tapped. To bridge the gap, in this paper, we propose HPT, a Hierarchy-aware Prompt Tuning method to handle HTC from a multi-label MLM perspective. Specifically, we construct a dynamic virtual template and label words that take the form of soft prompts to fuse the label hierarchy knowledge and introduce a zero-bounded multi-label cross entropy loss to harmonize the objectives of HTC and MLM. Extensive experiments show HPT achieves state-of-the-art performances on 3 popular HTC datasets and is adept at handling the imbalance and low resource situations. Our code is available at https://github.com/wzh9969/HPT.