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

Hwiyeol Jo, Hyunwoo Lee, Kang Min Yoo, Taiwoo Park
6/19/2024
cs.CLcs.AI

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Abstract

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.

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Cite this paper

@article{jo2024zerodl,
  title  = {ZeroDL: Zero-shot Distribution Learning for Text Clustering via Large Language Models},
  author = {Hwiyeol Jo and Hyunwoo Lee and Kang Min Yoo and Taiwoo Park},
  year   = {2024},
  eprint = {2406.13342},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.13342v2}
}

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