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Creators/Authors contains: "Boyd-Graber, Jordan Lee"

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  1. ; ; ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available January 1, 2026
  2. ; ; ; (, Association for Computational Linguistics)
    CAIMIRA discovers the skills that humans and AIs use to answer questions. By scraping websites where trivia nerds answer really difficult questions and posing those questions to AI models like GPT-4 and LLaMA-3-70B, while humans excel in knowledge-based abductive reasoning, AI outperforms on fact-based historical recall. This research suggests future challenges should focus on more complex reasoning and nuanced language tasks to better align AI development with human cognitive strengths. 
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  3. ; ; ; ; (, Association for Computational Linguistics)
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  4. ; ; ; ; ; (, 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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  5. ; ; ; ; ; ; (, Association for Computational Linguistics)
    Learning vocabulary (e.g., benevolent) can be tedious, but using mnemonics (e.g., benevolent sounds like "benefits," and a kind boss gives benefits) makes it more engaging and effective. This paper introduces SMART, a large language model trained to produce mnemonics based on feedback from flashcard learners. Students struggle to predict which mnemonics will help them most. Still, by training SMART on both student preferences and learning outcomes, we can generate mnemonics as effectively as GPT-4, but at a much lower cost. 
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