Leveraging Log Probabilities in Language Models to Forecast Future Events
This work addresses forecasting for strategic planning in various sectors, but it is incremental as it builds on previous research.
The paper tackles the problem of predicting future events using Large Language Models (LLMs) by introducing a method based on log probabilities, achieving a Brier score of 0.186 with a 26% improvement over random chance and a 19% improvement over existing AI systems.
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.