# Navigating Tomorrow: Reliably Assessing Large Language Models Performance on Future Event Prediction

arXiv: https://arxiv.org/abs/2501.05925 · HTML: https://mlsanity.com/paper/2501.05925/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2025.

## Built on by (most cited first, 5 of 5)

- [FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction](https://mlsanity.com/paper/2508.11987/) (2025)
- [CryptoBench: A Dynamic Benchmark for Expert-Level Evaluation of LLM Agents in Cryptocurrency](https://mlsanity.com/paper/2512.00417/) (2025)
- [Detecting Future-related Contexts of Entity Mentions](https://mlsanity.com/paper/2502.15332/) (2025)
- [A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts](https://mlsanity.com/paper/2606.27881/) (2026)
- [VepAgent: Bridging Causal-Transition via Tool-Augmented Reinforcement Learning for Video Event Prediction](https://mlsanity.com/paper/2610.06293/) (2026)

## Abstract (arXiv)

Predicting future events is an important activity with applications across multiple fields and domains. For example, the capacity to foresee stock market trends, natural disasters, business developments, or political events can facilitate early preventive measures and uncover new opportunities. Multiple diverse computational methods for attempting future predictions, including predictive analysis, time series forecasting, and simulations have been proposed. This study evaluates the performance of several large language models (LLMs) in supporting future prediction tasks, an under-explored domain. We assess the models across three scenarios: Affirmative vs. Likelihood questioning, Reasoning, and Counterfactual analysis. For this, we create a dataset1 by finding and categorizing news articles based on entity type and its popularity. We gather news articles before and after the LLMs training cutoff date in order to thoroughly test and compare model performance. Our research highlights LLMs potential and limitations in predictive modeling, providing a foundation for future improvements.