This study investigates whether OpenAI's ChatGPT-3.5 and ChatGPT-4 can
accurately forecast future events using two distinct prompting strategies. To
evaluate the accuracy of the predictions, we take advantage of the fact that
the training data at the time of experiment stopped at September 2021, and ask
about events that happened in 2022 using ChatGPT-3.5 and ChatGPT-4. We employed
two prompting strategies: direct prediction and what we call future narratives
which ask ChatGPT to tell fictional stories set in the future with characters
that share events that have happened to them, but after ChatGPT's training data
had been collected. Concentrating on events in 2022, we prompted ChatGPT to
engage in storytelling, particularly within economic contexts. After analyzing
100 prompts, we discovered that future narrative prompts significantly enhanced
ChatGPT-4's forecasting accuracy. This was especially evident in its
predictions of major Academy Award winners as well as economic trends, the
latter inferred from scenarios where the model impersonated public figures like
the Federal Reserve Chair, Jerome Powell. These findings indicate that
narrative prompts leverage the models' capacity for hallucinatory narrative
construction, facilitating more effective data synthesis and extrapolation than
straightforward predictions. Our research reveals new aspects of LLMs'
predictive capabilities and suggests potential future applications in
analytical contexts.

研究调查了使用两种不同的提示策略时，OpenAI 的 ChatGPT-3.5 和 ChatGPT-4 是否能准确预测未来事件。经过分析 100 个提示，发现未来故事提示显著提高了 ChatGPT-4 的预测准确性，特别是在主要奥斯卡奖项和经济趋势的预测中。这些发现表明，故事提示利用了模型的虚构叙事构建能力，比简单的预测更能有效地进行数据综合和推断。我们的研究揭示了 LLMs 预测能力的新方面，并暗示了在分析背景下的潜在未来应用。