Hindcast: Replaying Prediction Markets to Evaluate LLM Forecasters
Score
75首次提出并解决LLM预测评估中的两种信息泄漏问题，方法创新性强。
实验设计严谨，论证充分，但论文尚未经同行评审。
为AI预测评估提供标准化工具，对研究者和市场从业者均有实用价值。
首个版本于2025年7月发布，紧跟LLM预测研究热点。
来源为arXiv预印本，尚未正式发表，但来自可信机构。
Base 83 × T2 Source weight ×0.9 = 75
Curation line 70Selected
Why selected
High methodological innovation that rigorously addresses information leakage in LLM forecasting evaluation, with solid experimental design.
AI commentary
「This paper tackles the core flaw of LLM forecasting evaluation—information leakage—with an elegantly simple solution, Hindcast. If you work on AI forecasting, AI safety, or market analysis, this is a must-read. For those studying LLM capabilities, it helps distinguish genuine reasoning from mere recall.」
Quick overview
This paper introduces Hindcast, a method to fairly evaluate LLMs' forecasting ability. Traditional backtesting suffers from information leakage: LLMs can surface post-event reports or exploit training data containing future knowledge, turning foresight into recall. Hindcast closes both leaks by freezing a Reddit snapshot at a chosen past date t₀, allowing the model to read only posts before t₀. It replays resolved Polymarket prediction markets and compares forecasts against both outcomes and market prices at t₀. Experiments show that once leaks are closed, retrieval helps only when Reddit discussed the event beforehand; otherwise, it hurts performance.
- Traditional LLM forecasting evaluation suffers from two leakage channels: retrieving post-event reports and training data containing future knowledge.
- Hindcast eliminates both leaks by freezing a Reddit snapshot and setting per-market cutoffs, ensuring the test measures genuine foresight.
- Experiments show that with leaks closed, retrieval helps only when Reddit had prior discussion; otherwise it degrades accuracy.
- The evaluation is reusable on new markets without going stale.
Full text
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