Classifying Proposals of Decentralized Autonomous Organizations Using Large Language Models

Understand

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.