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WorldCup Arena: Prospective, Leakage-Free Evaluation of Frontier LLMs on a Live Tournament
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers evaluated six frontier LLMs on live 2026 FIFA World Cup predictions, collecting 4,494 scored forecasts across 104 matches with zero data leakage by design.
- 为何重要
- Engineers building or benchmarking LLMs should care about prospective evaluation methods that avoid memorization and test genuine forecasting capability on real-time events.
- 注意
- Models achieved 63.9% accuracy on match outcomes, matching bookmaker favorites, and showed narrow performance margins across systems with high agreement but low individual accuracy.
收听本摘要
- llm
- language model
- eval
- benchmark
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