ForecastBench

ForecastBench是由联邦储备银行芝加哥分行和预测研究机构等机构联合创建的一个动态预测能力评估基准数据集。该数据集包含1000个标准化预测问题，这些问题是从一个更大的实时问题库中随机抽取的。数据集的内容涵盖了从预测市场、预测平台和现实世界的时间序列中收集的新问题，每日更新以避免数据泄露。数据集的创建旨在评估机器学习系统在预测未来事件方面的准确性，特别是在避免已知答案的情况下。应用领域包括经济预测、投资决策和公共卫生事件预测等，旨在解决人类预测中的成本高、时间长、领域限制和偏见问题。

ForecastBench is a benchmark dataset for dynamic forecasting ability evaluation, jointly created by the Federal Reserve Bank of Chicago, forecasting research institutions and other relevant organizations. This dataset contains 1,000 standardized forecasting questions randomly sampled from a larger real-time question bank. The dataset covers newly collected questions from forecasting markets, forecasting platforms and real-world time series, and is updated daily to prevent data leakage. It is designed to evaluate the accuracy of machine learning systems in forecasting future events, especially when known answers are excluded. Its application fields include economic forecasting, investment decision-making, public health event forecasting and other scenarios, aiming to address the issues of high cost, prolonged time consumption, domain restrictions and biases in human forecasting.

搜集汇总

数据集介绍

构建方式

ForecastBench is meticulously constructed to evaluate the accuracy of machine learning systems on a standardized set of forecasting questions. The benchmark comprises 1,000 forecasting questions that are automatically generated and regularly updated from nine diverse data sources, including prediction markets, forecasting platforms, and real-world time series. To prevent data leakage, all questions pertain to future events with no known answers at the time of submission. The benchmark quantifies the forecasting abilities of current ML systems by collecting forecasts from expert human forecasters, the general public, and large language models (LLMs) on a random subset of 200 questions.

特点

ForecastBench stands out for its dynamic and continuously updated nature, ensuring that the benchmark remains relevant and challenging as ML models evolve. The benchmark's questions are sourced from a wide array of domains, providing a comprehensive evaluation of forecasting capabilities across different areas. Additionally, ForecastBench employs a rigorous methodology to avoid data contamination, ensuring that the test sets remain uncontaminated by post-training knowledge. The benchmark also features a public leaderboard that displays system and human scores, facilitating real-time comparisons and tracking of forecasting abilities.

使用方法

ForecastBench is designed to be used by researchers and practitioners to assess and compare the forecasting capabilities of various machine learning models, including large language models. Users can submit their forecasts to the benchmark and view their performance on the public leaderboard. The benchmark's datasets, including LLM and human forecasts, rationales, and accuracy, are available for future LLM fine-tuning and testing. Researchers can leverage these datasets to develop innovative approaches for improving AI-based forecasting systems, such as methods for continuously updating models with current events and enhancing LLMs to reason over extended time frames.

背景与挑战

背景概述

ForecastBench, introduced in 2024, is a pioneering dynamic benchmark designed to evaluate the forecasting capabilities of machine learning (ML) systems. Developed by a consortium of researchers from the Federal Reserve Bank of Chicago, the Forecasting Research Institute, New York University, the University of California, Berkeley, and the University of Pennsylvania, ForecastBench addresses the critical need for a standardized framework to assess the accuracy of ML systems in predicting future events. The dataset comprises 1,000 forecasting questions that are automatically generated and regularly updated, ensuring that the benchmark remains relevant and challenging. The creation of ForecastBench has significant implications for various decision-making processes, including economic forecasting and pandemic response, by providing a rigorous platform for comparing the performance of ML systems against human experts and the general public.

当前挑战

The primary challenge addressed by ForecastBench is the lack of a standardized evaluation framework for ML systems in forecasting tasks. The dynamic nature of the benchmark, which continuously updates with new questions about future events, presents several technical challenges. Firstly, ensuring that the questions are free from data leakage, meaning they have no known answers at the time of submission, is crucial. Secondly, the benchmark must accurately reflect the current state of ML capabilities, necessitating regular updates and maintenance. Additionally, the evaluation process must be robust enough to detect subtle manipulations or overfitting by model developers, who may have financial incentives to exaggerate their models' accuracy. Finally, the benchmark must provide a fair comparison between ML systems, human experts, and the general public, ensuring that all participants face the same information environment and evaluation criteria.

常用场景

经典使用场景

ForecastBench 数据集在评估机器学习系统在标准化预测问题上的准确性方面具有经典应用场景。通过提供一个动态更新的基准，该数据集允许研究人员和开发者持续跟踪和比较不同模型在预测未来事件上的表现。这种持续的评估机制使得 ForecastBench 成为衡量当前机器学习系统预测能力的重要工具，特别是在大规模预测任务中。

衍生相关工作

ForecastBench 数据集的引入催生了一系列相关研究和工作。例如，研究者们利用该数据集开发了新的预测模型和算法，以提升机器学习系统在预测任务中的表现。同时，基于 ForecastBench 的评估结果，学术界和工业界开始探索如何通过模型集成和数据增强技术进一步提高预测精度。此外，该数据集还激发了对预测任务中模型偏差和不确定性的深入研究，推动了预测科学的发展。

数据集最近研究

最新研究方向

在预测未来事件的领域中，ForecastBench数据集的最新研究方向主要集中在评估机器学习系统在标准化预测问题上的准确性。该数据集通过动态生成和定期更新的1000个预测问题，量化当前机器学习系统的能力，并与专家（人类）预测者、普通公众和大型语言模型（LLMs）的预测进行比较。研究结果表明，尽管LLMs在许多基准测试中表现出色，但在ForecastBench上，专家预测者的表现优于最先进的LLMs。这一发现为改进AI预测系统提供了重要方向，特别是在持续更新模型以反映当前事件和增强LLMs在长时间框架上推理能力方面。

相关研究论文

• ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities

  联邦储备银行芝加哥分行、预测研究机构、纽约大学、加州大学伯克利分校、宾夕法尼亚大学 · 2024年