Large language models predict the future by "hallucinating" stories, turning creative writing into a crystal ball that questions the nature of prediction itself.
Engaging creativity allows LLMs to extrapolate predictions from vast data sets. systems, trained on vast amounts of data, can engage in remarkably human-like conversations, answer questions, and even tackle complex tasks. Ahas shed light on a particularly intriguing aspect of LLMs: their ability to predict future events through the power of creative prompting and"hallucinatory" storytelling.
The results were fascinating. When prompted to create future narratives, particularly those featuring authoritative figures like Federal Reserve Chair Jerome Powell discussing past economic data from a future standpoint, ChatGPT-4 demonstrated a remarkable ability to make accurate predictions. Its forecasts for things like inflation rates were comparable to real-world consumer expectations surveys.
Narrative prompting, by weaving future events into fictional stories, appears to bypass certain constraints designed to align GPT-4’s outputs with OpenAI’s ethical guidelines, particularly those intended to prevent the generation of speculative, high-stakes predictions like those in financial or medical domains.
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