mlsanityData to 14 Oct 2026

FOReCAst: The Future Outcome Reasoning and Confidence Assessment Benchmark

arXiv 2502.19676 ↗First posted 27 Feb 2025cs.LG cs.CLcited by 8

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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
  1. 2023Evaluating Large Language Models: A Comprehensive Surveycited by 202
  2. 2020What Can We Learn from Collective Human Opinions on Natural Language Inference Data?cited by 153
  3. 2022The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluationcited by 139
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  6. 2020ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Datacited by 43
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 4.

8 papers.