Integrating local knowledge with ChatGPT-like large-scale language models for enhanced societal comprehension of carbon neutrality - Te Han; Rong-Gang Cong; Biying Yu; Baojun Tang; Yi-Ming Wei (Energy and AI)
Addressing carbon neutrality presents a multifaceted challenge, necessitating collaboration across various disciplines, fields, and societal stakeholders. With the increasing urgency to mitigate climate change, there is a crucial need for innovative approaches in communication and education to enhance societal understanding and engagement. Large-scale language models like ChatGPT emerge as transformative tools in the AI era, offering potential to revolutionize how we approach economic, technological, social, and environmental issues of achieving carbon neutrality. However, the full potential of these models in carbon neutrality is yet to be realized, hindered by limitations in providing detailed, localized, and expert-level insights across an expansive spectrum of subjects. To bridge these gaps, this paper introduces an innovative framework that integrates local knowledge with LLMs, aiming to markedly enhance the depth, accuracy, and regional relevance of the information provided. The effectiveness of this framework is examined from government, corporations, and community perspectives. The integration of local knowledge with LLMs not only enriches the AI’s comprehension of local specificities but also guarantees an up-to-date information that is crucial for addressing the specific concerns and questions about carbon neutrality raised by a broad array of stakeholders. Overall, the proposed framework showcases significant potential in enhancing societal comprehension and participation towards carbon neutrality.