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原文摘要

Asking questions is fundamental for deeper understanding, yet evaluating and generating questions that improve downstream outcomes remains a challenging problem. To address this gap, we introduce QUEST (Question Utility Estimation with Simulated Tests), a framework that offers a new outcome-driven paradigm for question evaluation and generation, instead of indirect metrics that are based on the underlying content of the question and its answer. QUEST uses language models to simulate learners that ask questions while studying a textbook chapter and quantifies the utility of a question as its direct contribution to understanding as measured by the learner's exam performance. To analyze the effectiveness of QUEST, we curate TEXTBOOK-EXAM, a benchmark that aligns textbook sections with exam questions across five academic disciplines. Using QUEST, we filter for high-utility questions and fine-tune question generators via rejection sampling and show that questions generated by QUEST-trained models improve simulated exam scores by 18.5% compared to the next strongest baseline. Furthermore, we demonstrate the practical real-world value of QUEST by showing that simulation-computed utility of a question is moderately positively correlated with its usefulness as perceived by human evaluators.