# Leveraging Log Probabilities in Language Models to Forecast Future Events

arXiv: https://arxiv.org/abs/2501.04880 · HTML: https://mlsanity.com/paper/2501.04880/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2025.

## Built on by (most cited first, 2 of 2)

- [Autograder+: A Multi-Faceted AI Framework for Rich Pedagogical Feedback in Programming Education](https://mlsanity.com/paper/2510.26402/) (2025)
- [Semantic Bayesian World Models](https://mlsanity.com/paper/2609.03834/) (2026)

## Abstract (arXiv)

In the constantly changing field of data-driven decision making, accurately predicting future events is crucial for strategic planning in various sectors. The emergence of Large Language Models (LLMs) marks a significant advancement in this area, offering advanced tools that utilise extensive text data for prediction. In this industry paper, we introduce a novel method for AI-driven foresight using LLMs. Building on top of previous research, we employ data on current trends and their trajectories for generating forecasts on 15 different topics. Subsequently, we estimate their probabilities via a multi-step approach based on log probabilities. We show we achieve a Brier score of 0.186, meaning a +26% improvement over random chance and a +19% improvement over widely-available AI systems.