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FOReCAst: The Future Outcome Reasoning and Confidence Assessment Benchmark

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arxiv 2502.19676 v4 pith:6U5HTP5I submitted 2025-02-27 cs.LG cs.CL

FOReCAst: The Future Outcome Reasoning and Confidence Assessment Benchmark

classification cs.LG cs.CL
keywords confidenceforecastingassessmentforecastbenchmarkcomprehensivefutureoutcome
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Forecasting is an important task in many domains, such as technology and economics. However existing forecasting benchmarks largely lack comprehensive confidence assessment, focus on limited question types, and often consist of artificial questions that do not align with real-world human forecasting needs. To address these gaps, we introduce FOReCAst (Future Outcome Reasoning and Confidence Assessment), a benchmark that evaluates models' ability to make predictions and their confidence in them. FOReCAst spans diverse forecasting scenarios involving Boolean questions, timeframe prediction, and quantity estimation, enabling a comprehensive evaluation of both prediction accuracy and confidence calibration for real-world applications.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning

    cs.CL 2026-06 unverdicted novelty 7.0

    WorldReasoner supplies 345 resolved forecasting tasks built from 14,141 articles to score LM agents on outcome quality, evidence quality, and reasoning quality against time-bounded evidence and hindsight graphs.