mlsanityData to 14 Oct 2026

ZeroDL: Zero-shot Distribution Learning for Text Clustering via Large Language Models

arXiv 2406.13342 ↗First posted 19 Jun 2024cs.CL cs.AIcited by 1

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Abstract (arXiv)

The advancements in large language models (LLMs) have brought significant progress in NLP tasks. However, if a task cannot be fully described in prompts, the models could fail to carry out the task. In this paper, we propose a simple yet effective method to contextualize a task toward a LLM. The method utilizes (1) open-ended zero-shot inference from the entire dataset, (2) aggregate the inference results, and (3) finally incorporate the aggregated meta-information for the actual task. We show the effectiveness in text clustering tasks, empowering LLMs to perform text-to-text-based clustering and leading to improvements on several datasets. Furthermore, we explore the generated class labels for clustering, showing how the LLM understands the task through data.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 19 Jun 2024.

Builds on

15 of 33 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  5. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  6. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,434
  7. 2024LLM2Vec: Large Language Models Are Secretly Powerful Text Encoderscited by 404
  8. 2021True Few-Shot Learning with Language Modelscited by 344
  9. 2023State of What Art? A Call for Multi-Prompt LLM Evaluationcited by 310
  10. 2022Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrationscited by 116
  11. 2023Large Language Models Enable Few-Shot Clusteringcited by 72
  12. 2023ClusterLLM: Large Language Models as a Guide for Text Clusteringcited by 70
3 more
  1. 2023Goal-Driven Explainable Clustering via Language Descriptionscited by 58
  2. 2022SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Featurescited by 33
  3. 2023IDAS: Intent Discovery with Abstractive Summarizationcited by 22

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Citing papers per year: 2026: 1.

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  1. 2026Universal Guideline-Driven Image Clustering via a Hybrid LLM Agentcited by 0