用大模型做德尔菲调查，预测生成式AI未来发展方向。
An LLM-based Delphi Study to Predict GenAI Evolution
- 用大模型模拟专家问答，开展多轮德尔菲调研。
- 发现地缘政治、经济差距和伦理问题影响生成式AI演进。
- 适合政策制定者与科技战略研究者参考。
预测复杂且快速演变系统的发展轨迹仍具挑战性，尤其在数据稀缺或不可靠的领域。本研究提出一种新方法，利用大语言模型开展德尔菲研究，探索生成式人工智能（Generative Artificial Intelligence）的未来发展。通过多轮模拟专家反馈，揭示了地缘政治紧张、经济不平等、监管框架和伦理考量等关键因素的影响。结果表明，基于大模型的德尔菲研究能有效促进结构化情景分析，整合多元观点并缓解受访者疲劳。然而，该方法存在知识截止、固有偏见及对初始条件敏感等问题。尽管提供了新颖的前瞻性分析路径，但仍需进一步研究以提升其处理异质性、增强可靠性，并整合外部数据源。
原文摘要 · Abstract (English)
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.
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