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

• URL: http://arxiv.org/abs/2406.13342v1
• Date: Wed, 19 Jun 2024 08:48:05 GMT
• Title: ZeroDL: Zero-shot Distribution Learning for Text Clustering via Large Language Models
• Authors: Hwiyeol Jo, Hyunwoo Lee, Taiwoo Park,
• Abstract summary: We propose a simple yet effective method to contextualize a task toward a specific large language model (LLMs) We show the effectiveness of this approach in text clustering tasks, and also highlight the importance of the contextualization through examples of the above procedure.
• Score: 5.011816280731356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent advancements in large language models (LLMs) have brought significant progress in solving NLP tasks. Notably, in-context learning (ICL) is the key enabling mechanism for LLMs to understand specific tasks and grasping nuances. In this paper, we propose a simple yet effective method to contextualize a task toward a specific LLM, by (1) observing how a given LLM describes (all or a part of) target datasets, i.e., open-ended zero-shot inference, and (2) aggregating the open-ended inference results by the LLM, and (3) finally incorporate the aggregated meta-information for the actual task. We show the effectiveness of this approach in text clustering tasks, and also highlight the importance of the contextualization through examples of the above procedure.

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