Title: Assessing Large Language Models in Updating Their Forecasts with New Information
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: future event prediction, forecasting, model probing

Abstract: Prior work has largely treated future event prediction as a static task, failing to consider how forecasts and the confidence in them should evolve as new evidence emerges. To address this gap, we introduce EvolveCast, a framework for evaluating whether large language models appropriately revise their predictions in response to new information. In particular, EvolveCast assesses whether LLMs adjust their forecasts when presented with information released after their training cutoff. We use human forecasters as a comparative reference to analyze prediction shifts and confidence calibration under updated contexts. While LLMs demonstrate some responsiveness to new information, their updates are often inconsistent or overly conservative, revealing limitations in their ability to incorporate it in their predictions. Across settings, models tend to express conservative bias, underscoring the need for more robust approaches to belief updating