Chunking Strategy for Retrieval Augmented Generation in Regulation Documents

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Amar Fadillah, Nuke Athahirah, Kuan Ting Lai

2024 11th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2024 Conference paper Cited by 2 Quartile

Abstract

Large language model (LLM) have been proven to be capable of performing various text processing tasks. LLMs have the potential to be used as a tool for analyzing regulation documents. To enhance the ability of LLMs to analyze regulation documents, we propose a regulation document chunking method that improves the accuracy of the analysis results. Our proposed method produces better accuracy than the sequential chunking method. © 2024 IEEE.

Affiliations

National Taipei University of Technology, College of Electrical Engineering and Computer Science, Taipei, Taiwan; Universitas Pendidikan Indonesia, Industrial Automation and Robotics Engineering Education, Bandung, Indonesia

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