Related papers: 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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