Predict the Future: A Review of Language Model Event Forecasting

What does it mean for a language model to predict the future? Reported forecasting performance can reflect different sources: historical outcome recall, timely evidence retrieval, reasoning about unresolved events, or access to human and market estimates. This review examines real-world event forecasting through the relationship between prediction targets, forecasting mechanisms, and the evidence used to evaluate them. We distinguish probabilistic forecasts, structured event predictions, and open-ended future generation, clarifying their temporal and resolution requirements. We synthesize retrieval, reasoning, aggregation, calibration, training, and agentic updating as interventions in the forecasting process, and examine the comparisons needed to attribute their benefits. We map existing benchmarks and selected performance results to explicit evaluation conditions, including information availability, forecast horizon, outcome resolution, scoring rules, and aggregation populations. A traceable result-level evidence map preserves the experimental context needed to interpret reported progress. This perspective separates access to predictive information from the ability to use it, and distinguishes probability quality from scenario coverage and decision utility. The review develops a research agenda for temporally valid evaluation, calibrated uncertainty, and reliable forecast updating, connecting methodological advances to the capabilities they can substantiate.