Text2DistBench is a new scalable benchmark showing LLMs outperform random baselines on distributional reading comprehension from YouTube comments but vary widely by question type and distribution characteristics.
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A model-free method builds confidence sets for latent parameters to proxy sim-to-real discrepancies and estimates the quantile function of that proxy to produce a distribution-level fidelity profile for simulators.
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Beyond Facts: Benchmarking Distributional Reading Comprehension in Large Language Models
Text2DistBench is a new scalable benchmark showing LLMs outperform random baselines on distributional reading comprehension from YouTube comments but vary widely by question type and distribution characteristics.
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Model-Free Assessment of Simulator Fidelity via Quantile Curves
A model-free method builds confidence sets for latent parameters to proxy sim-to-real discrepancies and estimates the quantile function of that proxy to produce a distribution-level fidelity profile for simulators.