Assessing How Large Language Models Can Be Integrated with or Used for Blockchain Technology: Overview and Illustrative Case Study

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Assessing How Large Language Models Can Be Integrated with or Used for Blockchain Technology: Overview and Illustrative Case Study

Abstract:
While Large Language Models (LLMs) are turning the field of natural language processing on its head with their remarkable ability to understand context and generate creative content, blockchain is currently an excellent option for building decentralized systems that are both secure and transparent. While these two technologies are already powerful tools individually, combining them can only be promising. In this article, we will explore the opportunities offered by integrating LLMs with Blockchain. By reviewing existing work and research and discussing use cases, this article evaluates the benefits and analyzes the challenges of LLMs in Blockchain-based applications. We also propose two possible architectures and outline our approach for integrating LLMs into decentralized applications as a tool capable of understanding the sometimes imprecise and ambiguous intentions of decentralized application users and translating them into clear and executable instructions for optimal transactions. This approach aims to improve and optimize the interaction between users and the blockchain by better understanding their intentions, which could pave the way for massive use and adoption of decentralized systems. This work therefore contributes to Blockchain by proposing state-of-the-art LLM integration perspectives while presenting a precise approach to this integration.
Date of Conference: July 19-22, 2023
Date Added to IEEE XploreDecember 25, 2023
Electrical and Computer Engineering, University of Kinshasa (UNIKIN), Kinshasa, Democratic Republic of Congo
Electrical and Computer Engineering, University of Kinshasa (UNIKIN), Kinshasa, Democratic Republic of Congo
Institute for Smart Systems Technologies, Universitaet Klagenfurt, Klagenfurt, Austria
Universitaet Klagenfurt /Inst. f Smart Systems Technologies, Austria
Polytechnic Faculty, University of Kinshasa, DR-Congo
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