mlsanityData to 14 Oct 2026

A Simple and Effective Framework for Strict Zero-Shot Hierarchical Classification

arXiv 2305.15282 ↗First posted 24 May 2023cs.CL cs.AIcited by 7

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Abstract (arXiv)

In recent years, large language models (LLMs) have achieved strong performance on benchmark tasks, especially in zero or few-shot settings. However, these benchmarks often do not adequately address the challenges posed in the real-world, such as that of hierarchical classification. In order to address this challenge, we propose refactoring conventional tasks on hierarchical datasets into a more indicative long-tail prediction task. We observe LLMs are more prone to failure in these cases. To address these limitations, we propose the use of entailment-contradiction prediction in conjunction with LLMs, which allows for strong performance in a strict zero-shot setting. Importantly, our method does not require any parameter updates, a resource-intensive process and achieves strong performance across multiple datasets.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 21 of its 28 references that are in the map. First posted on arXiv 24 May 2023.

Builds on

21 of 28 references.

References · 21

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  4. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,682
  5. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470
  6. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,434
  7. 2015Image-based Recommendations on Styles and Substitutescited by 706
  8. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 669
  9. 2019Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approachcited by 308
  10. 2021Surface Form Competition: Why the Highest Probability Answer Isn't Always Rightcited by 263
  11. 2022Impact of Pretraining Term Frequencies on Few-Shot Reasoningcited by 142
  12. 2017HDLTex: Hierarchical Deep Learning for Text Classificationcited by 106
9 more
  1. 2021Improving and Simplifying Pattern Exploiting Trainingcited by 97
  2. 2020Language Models are Open Knowledge Graphscited by 95
  3. 2016Yelp Dataset Challenge: Review Rating Predictioncited by 84
  4. 2022Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classificationcited by 52
  5. 2022HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classificationcited by 31
  6. 2022Zero-Shot Text Classification with Self-Trainingcited by 26
  7. 2022Exploiting Global and Local Hierarchies for Hierarchical Text Classificationcited by 15
  8. 2021Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoningcited by 9
  9. 2022Making Pretrained Language Models Good Long-tailed Learnerscited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 2.

7 papers.

  1. 2024TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervisioncited by 25
  2. 2024Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classificationcited by 16
  3. 2026Hierarchical Text Classification with LLM-Refined Taxonomiescited by 2
  4. 2024Misinformation with Legal Consequences (MisLC): A New Task Towards Harnessing Societal Harm of Misinformationcited by 1
  5. 2025Hierarchical Text Classification Using Black Box Large Language Modelscited by 1
  6. 2025The Advantage of Fine-Grained Trainingcited by 1
  7. 2026TEAMMix: Taxonomy Enrichment Augmentation and Minority-augmented Mixing Strategy for LLM-enhanced Weak-Supervised Hierarchical Text Classificationcited by 0