New Benchmark QuantSightBench Evaluates LLM Quantitative Forecasting with Prediction Intervals

A new standard named QuantSightBench has been launched to assess the abilities of large language models in quantitative forecasting. This benchmark tackles the shortcomings of current evaluations, which mainly emphasize judgment tasks in straightforward formats like binary or multiple-choice queries. However, actual forecasting in areas such as economics, public health, and social demographics necessitates numerical estimates for continuous quantities. To thoroughly evaluate this skill, the benchmark utilizes prediction intervals as its assessment method. These intervals require an understanding of scale, internal consistency at various confidence levels, and calibration across a range of outcomes, making them superior to point estimates for numerical forecasting. This method clarifies and tests uncertainty, encompassing a wider range of reasoning under uncertain conditions. This research is detailed in the arXiv preprint 2604.15859v1, announced as a cross-type submission. While forecasting has emerged as a key benchmark for reasoning under uncertainty, existing evaluations are still inadequate. The new benchmark seeks to address this deficiency by offering a more thorough evaluation of LLMs' forecasting capabilities.