FOReCAst: The Future Outcome Reasoning and Confidence Assessment Benchmark
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 27 Feb 2025.
Builds on
19 of 28 references.
References · 19
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7 more
- 2023Evaluating Large Language Models: A Comprehensive Surveycited by 202
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Built on by
Citing papers per year: 2025: 4; 2026: 4.
8 papers.
- 2025AIA Forecaster: Technical Reportcited by 9
- 2025Do Language Models Update their Forecasts with New Information?cited by 2
- 2025Future Is Unevenly Distributed: Forecasting Ability of LLMs Depends on What We're Askingcited by 2
- 2026ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgmentcited by 2
- 2025Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learningcited by 1
- 2026When AI Navigates the Fog of Warcited by 0
- 2026STQA: A Benchmark for Stock-Focused Tabular Question Answering over Historical and Forecasted Datacited by 0
- 2026CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inferencecited by 0
- 2026CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inference29 Sep 2026
- 2026STQA: A Benchmark for Stock-Focused Tabular Question Answering over Historical and Forecasted Data5 Sep 2026
- 2026ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment30 May 2026
- 2026When AI Navigates the Fog of War17 Mar 2026
- 2025Future Is Unevenly Distributed: Forecasting Ability of LLMs Depends on What We're Asking23 Nov 2025
- 2025AIA Forecaster: Technical Report10 Nov 2025
- 2025Do Language Models Update their Forecasts with New Information?28 Sep 2025
- 2025Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning31 Aug 2025