  Our study demonstrates the effective use of Large Language Models (LLMs) for
automating the classification of complex datasets. We specifically target
proposals of Decentralized Autonomous Organizations (DAOs), as the
clas-sification of this data requires the understanding of context and,
therefore, depends on human expertise, leading to high costs associated with
the task. The study applies an iterative approach to specify categories and
further re-fine them and the prompt in each iteration, which led to an accuracy
rate of 95% in classifying a set of 100 proposals. With this, we demonstrate
the po-tential of LLMs to automate data labeling tasks that depend on textual
con-text effectively.
