Future Is Unevenly Distributed: Forecasting Ability of LLMs Depends on What We're Asking

Chinmay Karkar, Paras Chopra

Future Is Unevenly Distributed: Forecasting Ability of LLMs Depends on What We're Asking: 2 upvotes on Hugging Face Daily Papers, #32 of 33 papers on 2025-11-26. Day-by-day upvote history.

Large Language Models (LLMs) demonstrate partial forecasting competence across social, political, and economic events. Yet, their predictive ability varies sharply with domain structure and prompt framing. We investigate how forecasting performance varies with different model families on real-world questions about events that happened beyond the model cutoff date. We analyze how context, question type, and external knowledge affect accuracy and calibration, and how adding factual news context modifies belief formation and failure modes. Our results show that forecasting ability is highly variable as it depends on what, and how, we ask.

Paper page on Hugging Face · arXiv

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