OpenEstimate Benchmark Tests LLMs on Real-World Uncertainty
Researchers have introduced OpenEstimate, a new benchmark designed to evaluate large language models (LLMs) on reasoning under uncertainty using real-world numerical estimation tasks. The benchmark addresses a critical gap in current evaluations, which typically focus on problems with well-defined answers. OpenEstimate requires models to synthesize background information and express predictions as probability distributions, mimicking real-world scenarios in healthcare, finance, and knowledge work where incomplete information is common. The benchmark is extensible and multi-domain, aiming to better characterize LLM performance in uncertain contexts. The work is detailed in a paper on arXiv (2510.15096).
Key facts
- OpenEstimate is a benchmark for evaluating LLMs on reasoning under uncertainty.
- It uses real-world numerical estimation tasks.
- Models must synthesize background information and express predictions as probability distributions.
- Current LLM evaluations focus on well-defined answers, creating a gap.
- The benchmark covers domains like healthcare, finance, and knowledge work.
- OpenEstimate is extensible and multi-domain.
- The paper is available on arXiv with ID 2510.15096.
- The work aims to better characterize LLM performance in uncertain contexts.
Entities
Institutions
- arXiv