Classifying Proposals of Decentralized Autonomous Organizations Using Large Language Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917711032877056 |
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| author | Ziegler, Christian Miranda, Marcos Cao, Guangye Arentoft, Gustav Nam, Doo Wan |
| author_facet | Ziegler, Christian Miranda, Marcos Cao, Guangye Arentoft, Gustav Nam, Doo Wan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07059 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Classifying Proposals of Decentralized Autonomous Organizations Using Large Language Models Ziegler, Christian Miranda, Marcos Cao, Guangye Arentoft, Gustav Nam, Doo Wan Computers and Society H.0 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. |
| title | Classifying Proposals of Decentralized Autonomous Organizations Using Large Language Models |
| topic | Computers and Society H.0 |
| url | https://arxiv.org/abs/2401.07059 |