LLM-as-a-Prophet: Understanding AI's Predictive Intelligence with Prophet Arena

Qingchuan Yang, Simon Mahns, Sida Li, Anri Gu, Jibang Wu, Haifeng Xu

Keywords: Forecasting, LLM Benchmark, LLM-as-a-Prophet, LLM Evaluation

TL;DR: We introduce Prophet Arena, a benchmark that evaluates LLM forecasting abilities, and systematically analyze strengths and gaps in AI predictive intelligence.

Abstract: With the rapid progress of large language models (LLMs) trained on every available piece of data, it becomes increasingly challenging to reliably evaluate their intelligence due to potential data contamination and benchmark overfitting. To overcome these challenges, we investigate a new angle of benchmarking LLMs' intelligence by evaluating their capability in forecasting real-world future events, a paradigm we call ``LLM-as-a-Prophet''. Such forecasting tasks require combination of sophisticated capabilities while remaining free from data contamination or overfitting. To systematically evaluate such predictive intelligence of LLMs, we introduce Prophet Arena, a general evaluation benchmark that continuously collects live forecasting tasks and decomposes each task into distinct pipeline stages, supporting 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 even in frontier models, such as inaccurate event recalls, misunderstanding of data sources and slower information aggregation compared to markets when resolution nears.

Track: Long Paper

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Submission Number: 43