ZeroDL: Zero-shot Distribution Learning for Text Clustering via Large Language Models
<|reference_start|>ZeroDL: Zero-shot Distribution Learning for Text Clustering via Large Language Models: 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.<|reference_end|>
@article{jo2024zerodl:,
    title={ZeroDL: Zero-shot Distribution Learning for Text Clustering via Large
  Language Models},
    author={Hwiyeol Jo, Hyunwoo Lee, Taiwoo Park},
    journal={arXiv preprint arXiv:2406.13342},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13342},
    primaryClass={cs.CL cs.AI}
}