An LLM-based Delphi Study to Predict GenAI Evolution
It addresses the problem of qualitative forecasting in domains with scarce data for researchers and policymakers, though it is incremental as it adapts an existing method (Delphi studies) with new technology (LLMs).
This study tackled the challenge of predicting the future of complex systems by using Large Language Models to conduct Delphi studies for forecasting the evolution of Generative AI, revealing insights into factors like geopolitical tensions and ethical considerations while highlighting limitations such as biases and knowledge cutoffs.
Predicting the future trajectory of complex and rapidly evolving systems remains a significant challenge, particularly in domains where data is scarce or unreliable. This study introduces a novel approach to qualitative forecasting by leveraging Large Language Models to conduct Delphi studies. The methodology was applied to explore the future evolution of Generative Artificial Intelligence, revealing insights into key factors such as geopolitical tensions, economic disparities, regulatory frameworks, and ethical considerations. The results highlight how LLM-based Delphi studies can facilitate structured scenario analysis, capturing diverse perspectives while mitigating issues such as respondent fatigue. However, limitations emerge in terms of knowledge cutoffs, inherent biases, and sensitivity to initial conditions. While the approach provides an innovative means for structured foresight, this method could be also considered as a novel form of reasoning. further research is needed to refine its ability to manage heterogeneity, improve reliability, and integrate external data sources.