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Creators/Authors contains: "Sung, Yoo Yeon"

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  1. ; ; ; ; (, Association for Computational Linguistics)
    As AI use becomes more common, it's important to measure not just whether the systems are correct but whether they know when they're incorrect. We propose a new metric to measure this mismatch between correctness and confidence, compare computer ability with human ability, and show that computers have a long way to go before they're well-calibrated. 
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    Free, publicly-accessible full text available July 27, 2026
  2. ; ; ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available January 1, 2026
  3. ; ; ; ; ; (, Association for Computational Linguistics)
    Many of the questions for training AIs how to answer questions come from the queries users type into search engines (like Google's Natural Questions). Is there a cheaper---perhaps even better---way? We propose a "naturalization" technique to turn high-quality, rigorously edited trivia questions into examples that resemble Natural Questions. Training on our naturalized questions and testing on natural questions comes close to the results with using Natural Questions, and we can improve results on MMLU (a standard modern evaluation set) by using our data. 
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