I am a full professor in the University of Maryland Computer Science Department (tenure home), Institute of Advanced Computer Studies, iSchool, and Language Science Center.

My research focuses on making machine learning more useful, more interpretable, and able to learn and interact from humans. This helps users sift through decades of documents; discover when individuals lie, reframe, or change the topic in a conversation; or to compete against humans in games that are based in natural language.

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Recent Publications

  • Srikanth, Neha Pundlik, Rupak Sarkar, Mane, Heran Y., Aparicio, Elizabeth M., Nguyen, Quynh C., Rachel Rudinger, and Jordan Boyd-Graber. Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering. North American Association for Computational Linguistics, 2024. [Code and Data] [Bibtex]
  • Chenglei Si, Navita Goyal, Tongshuang Wu, Chen Zhao, Shi Feng, Hal Daumé III, and Jordan Lee Boyd-Graber. Large Language Models Help Humans Verify Truthfulness---Except When They Are Convincingly Wrong. North American Association for Computational Linguistics, 2024. [Bibtex]
  • Alvin Grissom II, Jo Shoemaker, Benjamin Goldman, Ruikang Shi, Craig Stewart, C. Anton Rytting, Leah Findlater, Jordan Boyd-Graber, Wenyan Li, Alvin Grissom II, and Jordan Boyd-Graber. Rapidly Piloting Real-time Linguistic Assistance for Simultaneous Interpreters with Untrained Bilingual Surrogates. Linguistic Resources and Evaluation Conference, 2024. [Bibtex]
  • Quynh C. Nguyen, Elizabeth M. Aparicio, Michelle Jasczynski, Amara Channell Doig, Xiaohe Yue, Heran Mane, Neha Pundlik Srikanth, Francia Ximena Marin Gutierrez, Nataly Delcid, Xin He, and Jordan Boyd-Graber. Randomized Pilot of Rosie, a Health Education Question-and-Answer Chatbot for New Mothers. Journal of Medical Internet Research: Journal of Formative Research, 2024. [Bibtex]
  • Zongxia Li, Andrew Mao, Daniel Kofi Stephens, Pranav Goel, Emily Walpole, Juan Francisco Fung, Alden Dima, and Jordan Lee Boyd-Graber. TENOR: Topic Enabled Neural Organization and Recommendation: Evaluating Topic Models in Task Based Settings. European Association for Computational Linguistics, 2024. [Bibtex]
  • Ishani Mondal, Shwetha S, Anandhavelu Natarajan, Aparna Garimella, Sambaran Bandyopadhyay, and Jordan Lee Boyd-Graber. Presentations by the People, for the People: Harnessing LLMs for Generating Persona-Aware Slides from Documents. European Association for Computational Linguistics, 2024. [Bibtex]
  • Nishant Balepur, Matthew Shu, Alexander Hoyle, Alison Robey, Shi Feng, Seraphina Goldfarb-Tarrant, and Jordan Boyd-Graber. A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick. ArXiv, Preprint. [Bibtex]
  • Yoo Yeon Sung, Eve Fleisig, Ishani Mondal, and Jordan Lee Boyd-Graber. ADVSCORE: A Metric for the Evaluation and Creation of Adversarial Benchmarks. ArXiv, Preprint. [Bibtex]
  • Xiyang Wu, Tianrui Guan, Dianqi Li, Shuaiyi Huang, Xiaoyu Liu, Xijun Wang, Ruiqi Xian, Abhinav Shrivastava, Furong Huang, Jordan Lee Boyd-Graber, Tianyi Zhou, and Dinesh Manocha. AUTOHALLUSION: Automatic Generation of Hallucination Benchmarks for Vision-Language Models. ArXiv, Preprint. [Bibtex]
  • Matthew Shu, Nishant Balepur, Shi Feng, and Jordan Boyd-Graber. KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students. ArXiv, Preprint. [Bibtex]
  • Benjamin Börschinger, Jordan Boyd-Graber, Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Michelle Chen Huebscher, Wojciech Gajewski, Yannic Kilcher, Rodrigo Nogueira, and Lierni Sestorain Saralegu. Meta Answering for Machine Reading. ArXiv, Preprint. [Preprint] [Bibtex]
  • Zongxia Li, Ishani Mondal, Huy Nghiem, Yijun Liang, and Jordan Lee Boyd-Graber. PEDANTS (Precise Evaluations of Diverse Answer Nominee Text for Skinflints): Use Evaluation Metrics Wisely–Efficient Evaluation Analysis and Benchmarking for Open-Domain Question Answering. ArXiv, Preprint. [Bibtex]
  • Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, and Jordan Boyd-Graber. Quizbowl: The Case for Incremental Question Answering. ArXiv, Preprint. [Webpage] [Bibtex]
Jordan Boyd-Graber