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.

我们提出了一种简单但有效的方法来将任务上下文化为特定的大语言模型，通过观察给定的大语言模型如何描述目标数据集，聚合大语言模型的开放式推理结果，并最终将聚合的元信息纳入实际任务中，我们展示了这种方法在文本聚类任务中的有效性，并通过上述过程的示例突出了上下文化的重要性。