Forecasts of future events are essential inputs into informed decision-making. Machine learning (ML) systems have the potential to deliver forecasts at scale, but there is no framework for evaluating the accuracy of ML systems on a standardized set of forecasting questions. To address this gap, we introduce ForecastBench: a dynamic benchmark that evaluates the accuracy of ML systems on an automatically generated and regularly updated set of 1,000 forecasting questions. To avoid any possibility of data leakage, ForecastBench is comprised solely of questions about future events that have no known answer at the time of submission. We quantify the capabilities of current ML systems by collecting forecasts from expert (human) forecasters, the general public, and LLMs on a random subset of questions from the benchmark ($N=200$). While LLMs have achieved super-human performance on many benchmarks, they perform less well here: expert forecasters outperform the top-performing LLM (p-value $<0.01$). We display system and human scores in a public leaderboard at www.forecastbench.org.

翻译：对未来事件的预测是做出明智决策的关键输入。机器学习（ML）系统有潜力大规模提供预测，但目前缺乏一个框架来评估ML系统在一组标准化预测问题上的准确性。为弥补这一空白，我们引入了ForecastBench：这是一个动态基准，用于评估ML系统在自动生成并定期更新的1,000个预测问题集上的准确性。为避免任何数据泄露的可能性，ForecastBench仅包含关于提交时尚未有已知答案的未来事件的问题。我们通过收集专家（人类）预测者、公众以及大型语言模型（LLMs）在基准中随机抽取的问题子集（$N=200$）上的预测，来量化当前ML系统的能力。尽管LLMs在许多基准测试中已实现超人类性能，但在此处表现欠佳：专家预测者的表现优于表现最佳的LLM（p值 $<0.01$）。我们在 www.forecastbench.org 的公共排行榜上展示了系统和人类的得分。