Integrating Large Language Model and Logic Programming for Tracing Renewable Energy Use Across Supply Chain Networks

Abstract

Global warming is a critical issue today, largely due to the widespread use of fossil fuels in everyday life. One promising solution to reduce reliance on conventional energy sources is to promote the use of renewable power. In particular, to encourage the use of renewable energy in industrial sectors which involve development and manufacture of the industrial artifacts, there is continuous demand for tracing energy sources within the production processes. However, given a sophisticated industrial product that involves diverse and extensive components and their suppliers, the traceability analysis across its production is a critical challenge for ensuring the full utilization of renewable energy. To alleviate this issue, this paper presents a functional framework to support tracing the usage of renewable energy by integrating the Large Language Models (LLMs) and logic programming across supply chain networks. Specifically, the proposed framework contains the following components: (1) adopting graph-based models to process and manage the extensive information within supply chain networks; (2) using the Retrieval-Augmented Generation (RAG) techniques to support the LLM for processing the information related to supply chain networks and generating relevant responses with structured representations; and (3) presenting a logic programming-based solution to support the traceability analysis of renewable energy regarding the responses from the LLM. As a case study, we use a public dataset to evaluate the proposed framework by comparing it to the RAG-based LLM and its variant. Compared to baseline methods solely relying on LLMs, the experiments show that the proposed framework achieves significant improvement.