Forecasting Future World Events with Neural Networks (2206.15474v2)

Abstract: Forecasting future world events is a challenging but valuable task. Forecasts of climate, geopolitical conflict, pandemics and economic indicators help shape policy and decision making. In these domains, the judgment of expert humans contributes to the best forecasts. Given advances in LLMing, can these forecasts be automated? To this end, we introduce Autocast, a dataset containing thousands of forecasting questions and an accompanying news corpus. Questions are taken from forecasting tournaments, ensuring high quality, real-world importance, and diversity. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts (avoiding leakage from the future). Motivated by the difficulty of forecasting numbers across orders of magnitude (e.g. global cases of COVID-19 in 2022), we also curate IntervalQA, a dataset of numerical questions and metrics for calibration. We test LLMs on our forecasting task and find that performance is far below a human expert baseline. However, performance improves with increased model size and incorporation of relevant information from the news corpus. In sum, Autocast poses a novel challenge for LLMs and improved performance could bring large practical benefits.

Summary of "Forecasting Future World Events with Neural Networks"

The paper "Forecasting Future World Events with Neural Networks" presents a novel dataset and investigation into the applicability of neural networks for automating the forecasting of future world events. Recognizing the crucial role forecasting plays in policy-making across domains like climate change, geopolitical conflicts, pandemics, and economic indicators, the authors introduce Autocast, a dataset devised to test and enhance the capability of LLMs in prediction tasks traditionally dominated by human judgment.

Dataset and Experimental Set-Up

Autocast consists of thousands of publicly available forecasting questions from tournaments such as Metaculus and Good Judgment Open. Questions stem from varied domains—politics, economics, science, and more—and involve predicting outcomes on timescales ranging from days to decades. The unique aspect of this dataset is its temporal organization, which includes an accompanying news corpus from Common Crawl. This setup allows researchers to benchmark ML models by mimicking the human forecasters' process, which is based on information available prior to event resolutions, thus preventing the future data leakage problem in training models.

The paper also includes the IntervalQA dataset, which addresses the challenges of quantitative forecasting by testing models' ability to handle numerical predictions across different scales, further assessing their calibration precision.

Key Findings

The analysis indicates that current LLMs significantly underperform compared to human experts in forecasting accuracy. While the best model achieved a 65% accuracy in predicting binary outcomes, this is noticeably lower than the 92% accuracy achieved by human experts, as averaged in the Autocast dataset. However, model performance did show improvements proportional to model size and when incorporating information retrieved from the curated news corpus.

The authors make notable findings in model calibration, particularly when forecasting numerical quantities, which often span several orders of magnitude. The fidelity of uncertainty quantification is critical in such scenarios. Here, the paper introduces the RMS Calibration Error metric to evaluate the model’s ability to provide well-calibrated predictions.

Contributions and Implications

1. Dataset Introduction: Autocast is a significant contribution as it bridges the gap between model-centric benchmarks and real-world, domain-relevant forecasting challenges.
2. Temporal Context Simulation: The coupled news corpus enables rigorous retrodiction, evaluating models within constrained, realistic historical contexts, avoiding the enhancement from future knowledge.
3. Model Calibration and Uncertainty Quantification: Through IntervalQA, the authors probe the detailed aspects of prediction calibration, pushing for developments in models that can reliably handle uncertainty over varying contextual domains.

Implications for Future Research

Practically, advancements in this domain could lead to automated systems that assist in policy and risk assessment, especially in uncertainty-prone environments like geopolitical forecasting. Theoretically, the paper opens avenues for refining LLMs not solely to enhance their predictive power but to improve their probabilistic reasoning and calibration.

Future developments might focus on establishing more sophisticated retrieval mechanisms or integrating better training strategies to leverage interim crowdsourced data effectively. Additionally, further exploration into dynamically updating models, which incorporate new information continuously, could bring significant improvements in real-time forecasting scenarios.

Overall, while neural network-based forecasting is currently behind human capability in this task, the introduction of Autocast represents a forward step in aligning ML forecasting potential with human-level expertise. The research thus sets a foundational benchmark for future advances in AI-driven decision support systems.