LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena

• URL: http://arxiv.org/abs/2510.17638v1
• Date: Mon, 20 Oct 2025 15:20:05 GMT
• Title: LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena
• Authors: Qingchuan Yang, Simon Mahns, Sida Li, Anri Gu, Jibang Wu, Haifeng Xu,
• Abstract summary: Large language models (LLMs) are trained on Internet-scale data to forecast future events.<n>This paper systematically investigates such predictive intelligence of LLMs.<n>We uncover key bottlenecks towards achieving superior predictive intelligence via LLM-as-a-Prophet.
• Score: 25.304644327116975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Forecasting is not only a fundamental intellectual pursuit but also is of significant importance to societal systems such as finance and economics. With the rapid advances of large language models (LLMs) trained on Internet-scale data, it raises the promise of employing LLMs to forecast real-world future events, an emerging paradigm we call "LLM-as-a-Prophet". This paper systematically investigates such predictive intelligence of LLMs. To this end, we build Prophet Arena, a general evaluation benchmark that continuously collects live forecasting tasks and decomposes each task into distinct pipeline stages, in order to support our controlled and large-scale experimentation. Our comprehensive evaluation reveals that many LLMs already exhibit impressive forecasting capabilities, reflected in, e.g., their small calibration errors, consistent prediction confidence and promising market returns. However, we also uncover key bottlenecks towards achieving superior predictive intelligence via LLM-as-a-Prophet, such as LLMs' inaccurate event recalls, misunderstanding of data sources and slower information aggregation compared to markets when resolution nears.

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