New benchmark FinBench evaluates LLM calibration in financial forecasting

Researchers have introduced FinBench, a new benchmark designed to evaluate the calibration and uncertainty quality of large language models (LLMs) in financial forecasting. Unlike existing benchmarks that focus on semantic understanding or point accuracy, FinBench specifically addresses the temporal constraints and non-stationarity of real markets by being strictly time-gated to prevent look-ahead bias. The benchmark utilizes strictly proper scoring rules, such as the Brier score and Winkler interval score, to penalize overconfidence and assess how well models predict probabilities and prediction intervals for financial returns. AI

IMPACT This benchmark could lead to more reliable AI systems in finance by improving how LLMs handle uncertainty and temporal data.

RANK_REASON The item describes a new benchmark for evaluating LLMs in a specific domain (financial forecasting), which is a research contribution. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• Brier score
• CatalyzeX
• DagsHub
• FinBench
• Gotit.pub
• Hugging Face
• LLMs
• ScienceCast
• Winkler interval score

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