Our experts
AIM Faculty & Staff
Jordan Boyd-Graber
Jordan Boyd-Graber
Professor, Computer Science/UMIACS/Language Science Center/College of Information
Latest Papers
Language Models Don't Know What You Want: Evaluating Personalization in Deep Research Needs Real Users
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Association for Computational Linguistics
UMD Author(s): Jordan Boyd-Graber
Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators
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Findings of the Association for Computational Linguistics
UMD Author(s): Jordan Boyd-Graber
Measuring User's Mental Models of Speech Translation in Human-MT Collaboration
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Association for Computational Linguistics
UMD Author(s): Jordan Boyd-Graber
AI, Take the Wheel: What Drives Delegation and Trust in Human-Computer Cooperative Question Answering?
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Findings of the Association for Computational Linguistics
UMD Author(s): Jordan Boyd-Graber
AUDITA: A New Dataset to Audit Whether Humans or AI are Better at Audio QA
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Findings of the Association for Computational Linguistics
UMD Author(s): Jordan Boyd-Graber
BenchMarker: An Education-Inspired Toolkit for Highlighting Flaws in Multiple-Choice Benchmarks
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Association for Computational Linguistics
UMD Author(s): Jordan Boyd-Graber
No Questions are Stupid and but some are Poorly Posed: Understanding Poorly-Posed Information-Seeking Questions
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Association for Computational Linguistics
UMD Author(s): Jordan Boyd-Graber
Discrepancy Detection at the Data Level: Toward Consistent Multilingual Question Answering
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Empirical Methods in Natural Language Processing
UMD Author(s): Jordan Boyd-Graber
Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above
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Association for Computational Linguistics
UMD Author(s): Jordan Boyd-Graber
Personalized Help for Optimizing Low-Skilled Users' Strategy
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Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics
UMD Author(s): Jordan Boyd-Graber