About

Bonnie J. Dorr is a Professor of Computer Science at the University of Florida and a member of the Florida Institute for National Security (FINS). She leads the Natural Language Processing (NLP) Research Laboratory, where her work centers on artificial intelligence methods for deep language understanding, multilingual processing, and explainable AI. Before joining UF, Dr. Dorr served as Associate Director and Senior Research Scientist at the Florida Institute for Human and Machine Cognition (IHMC), following a distinguished 24-year career as a Professor of Computer Science at the University of Maryland. During her tenure at Maryland, she also served as Associate Dean of the College of Computer, Mathematical, and Natural Sciences. She later became a Program Manager at DARPA, overseeing human language technology programs advancing natural language understanding and translation. Dr. Dorr's primary research areas include artificial intelligence and natural language processing. She holds a Ph.D., an M.S., and a B.A. in Computer Science from the Massachusetts Institute of Technology and Boston University, respectively. She is a Fellow of the ACM, AAAI, and ACL, and has received numerous awards including the NSF Presidential Faculty (PECASE) Fellowship and a Sloan Research Fellowship. Dr. Dorr is a past President of the Association for Computational Linguistics and has been involved in DARPA’s Information Science and Technology (ISAT) Study Group.

Research topics

• Computer Science
• Artificial Intelligence
• Natural Language Processing
• Data science
• World Wide Web
• Psychology
• Information Retrieval
• Machine Learning
• Social psychology
• Engineering
• Linguistics

Selected publications

• BLADE: Better Language Answers through Dialogue and Explanations

  arXiv (Cornell University) · 2026-01-31

  articleOpen accessSenior author

  Large language model (LLM)-based educational assistants often provide direct answers that short-circuit learning by reducing exploration, self-explanation, and engagement with course materials. We present BLADE (Better Language Answers through Dialogue and Explanations), a grounded conversational assistant that guides learners to relevant instructional resources rather than supplying immediate solutions. BLADE uses a retrieval-augmented generation (RAG) framework over curated course content, dynamically surfacing pedagogically relevant excerpts in response to student queries. Instead of delivering final answers, BLADE prompts direct engagement with source materials to support conceptual understanding. We conduct an impact study in an undergraduate computer science course, with different course resource configurations and show that BLADE improves students' navigation of course resources and conceptual performance compared to simply providing the full inventory of course resources. These results demonstrate the potential of grounded conversational AI to reinforce active learning and evidence-based reasoning.

  PublisherOA PDF

• BLADE: Better Language Answers through Dialogue and Explanations

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-05

  preprintOpen accessSenior author

  Large language model (LLM)-based educational assistants often provide direct answers that short-circuit learning by reducing exploration, self-explanation, and engagement with course materials. We present BLADE (Better Language Answers through Dialogue and Explanations), a grounded conversational assistant that guides learners to relevant instructional resources rather than supplying immediate solutions. BLADE uses a retrieval-augmented generation (RAG) framework over curated course content, dynamically surfacing pedagogically relevant excerpts in response to student queries. Instead of delivering final answers, BLADE prompts direct engagement with source materials to support conceptual understanding. We conduct an impact study in an undergraduate computer science course, with different course resource configurations and show that BLADE improves students' navigation of course resources and conceptual performance compared to simply providing the full inventory of course resources. These results demonstrate the potential of grounded conversational AI to reinforce active learning and evidence-based reasoning.

  PublisherDOI

• “I Still Need Your Help”: Online information seeking behavior of International Students in the United States on Reddit

  PLoS ONE · 2026-02-09 · 1 citations

  articleOpen accessSenior author

  This study examines the online information-seeking behavior of international students in the United States. Following the onset of COVID-19, their need for timely and relevant information becomes critical. Despite greater challenges than domestic students, limited research explores how international students use online platforms to meet their unique information needs. With online communities being essential sources of information and bridges for online social capital, our study analyzes the r/f1visa subreddit to examine international students' information-seeking patterns before and during the COVID-19 pandemic. Additionally, we identify unmet information needs through members' interactions and recurring questions. Our analysis reveals a shift in topics, with pandemic discussions focusing on travel, financial difficulties, and entry concerns, while pre-pandemic conversations primarily about employment. The increased similarity among recurring questions during the pandemic suggests a convergence of shared struggles that fosters solidarity and emotional support, even as many informational needs remain inadequately addressed. By examining international students' information needs through the theoretical lens of online social capital, this study contributes to understanding how crisis conditions reshape the dynamics of online communities, blurring traditional distinctions between bonding and bridging capital. The findings can inform universities, policymakers, and online community designers in developing more responsive and inclusive information environments that recognize both the instrumental and emotional support functions of digital platforms for international students.

  PublisherDOI

• BLADE: Better Language Answers through Dialogue and Explanations

  Open MIND · 2026-02-05

  preprintSenior author

  Large language model (LLM)-based educational assistants often provide direct answers that short-circuit learning by reducing exploration, self-explanation, and engagement with course materials. We present BLADE (Better Language Answers through Dialogue and Explanations), a grounded conversational assistant that guides learners to relevant instructional resources rather than supplying immediate solutions. BLADE uses a retrieval-augmented generation (RAG) framework over curated course content, dynamically surfacing pedagogically relevant excerpts in response to student queries. Instead of delivering final answers, BLADE prompts direct engagement with source materials to support conceptual understanding. We conduct an impact study in an undergraduate computer science course, with different course resource configurations and show that BLADE improves students' navigation of course resources and conceptual performance compared to simply providing the full inventory of course resources. These results demonstrate the potential of grounded conversational AI to reinforce active learning and evidence-based reasoning.

  DOI

• Semantic Similarity is a Spurious Measure of Comic Understanding: Lessons Learned from Hallucinations in a Benchmarking Experiment

  arXiv (Cornell University) · 2026-03-02

  preprintOpen accessSenior author

  A system that enables blind or visually impaired users to access comics/manga would introduce a new medium of storytelling to this community. However, no such system currently exists. Generative vision-language models (VLMs) have shown promise in describing images and understanding comics, but most research on comic understanding is limited to panel-level analysis. To fully support blind and visually impaired users, greater attention must be paid to page-level understanding and interpretation. In this work, we present a preliminary benchmark of VLM performance on comic interpretation tasks. We identify and categorize hallucinations that emerge during this process, organizing them into generalized object-hallucination taxonomies. We conclude with guidance on future research, emphasizing hallucination mitigation and improved data curation for comic interpretation.

  PublisherDOI

• Semantic Similarity is a Spurious Measure of Comic Understanding: Lessons Learned from Hallucinations in a Benchmarking Experiment

  ArXiv.org · 2026-03-02

  articleOpen accessSenior author

  A system that enables blind or visually impaired users to access comics/manga would introduce a new medium of storytelling to this community. However, no such system currently exists. Generative vision-language models (VLMs) have shown promise in describing images and understanding comics, but most research on comic understanding is limited to panel-level analysis. To fully support blind and visually impaired users, greater attention must be paid to page-level understanding and interpretation. In this work, we present a preliminary benchmark of VLM performance on comic interpretation tasks. We identify and categorize hallucinations that emerge during this process, organizing them into generalized object-hallucination taxonomies. We conclude with guidance on future research, emphasizing hallucination mitigation and improved data curation for comic interpretation.

  PublisherOA PDF

• BLADE: Better Language Answers through Dialogue and Explanations

  arXiv (Cornell University) · 2026-01-31

  preprintOpen accessSenior author

  Large language model (LLM)-based educational assistants often provide direct answers that short-circuit learning by reducing exploration, self-explanation, and engagement with course materials. We present BLADE (Better Language Answers through Dialogue and Explanations), a grounded conversational assistant that guides learners to relevant instructional resources rather than supplying immediate solutions. BLADE uses a retrieval-augmented generation (RAG) framework over curated course content, dynamically surfacing pedagogically relevant excerpts in response to student queries. Instead of delivering final answers, BLADE prompts direct engagement with source materials to support conceptual understanding. We conduct an impact study in an undergraduate computer science course, with different course resource configurations and show that BLADE improves students' navigation of course resources and conceptual performance compared to simply providing the full inventory of course resources. These results demonstrate the potential of grounded conversational AI to reinforce active learning and evidence-based reasoning.

  PublisherDOI

• From Disagreement to Understanding: The Case for Ambiguity Detection in NLI

  2025-01-01

  articleOpen accessSenior author

  This position paper argues that annotation disagreement in Natural Language Inference (NLI) is not mere noise but often reflects meaningful variation, especially when triggered by ambiguity in the premise or hypothesis.While underspecified guidelines and annotator behavior contribute to variation, content-based ambiguity provides a process-independent signal of divergent human perspectives.We call for a shift toward ambiguity-aware NLI that first identifies ambiguous input pairs, classifies their types, and only then proceeds to inference.To support this shift, we present a framework that incorporates ambiguity detection and classification prior to inference.We also introduce a unified taxonomy that synthesizes existing taxonomies, illustrates key subtypes with examples, and motivates targeted detection methods that better align models with human interpretation.Although current resources lack datasets explicitly annotated for ambiguity and subtypes, this gap presents an opportunity: by developing new annotated resources and exploring unsupervised approaches to ambiguity detection, we enable more robust, explainable, and human-aligned NLI systems.

  PublisherOA PDFDOI

• From Disagreement to Understanding: The Case for Ambiguity Detection in NLI

  ArXiv.org · 2025-07-20

  preprintOpen accessSenior author

  This position paper argues that annotation disagreement in Natural Language Inference (NLI) is not mere noise but often reflects meaningful variation, especially when triggered by ambiguity in the premise or hypothesis. While underspecified guidelines and annotator behavior contribute to variation, content-based ambiguity provides a process-independent signal of divergent human perspectives. We call for a shift toward ambiguity-aware NLI that first identifies ambiguous input pairs, classifies their types, and only then proceeds to inference. To support this shift, we present a framework that incorporates ambiguity detection and classification prior to inference. We also introduce a unified taxonomy that synthesizes existing taxonomies, illustrates key subtypes with examples, and motivates targeted detection methods that better align models with human interpretation. Although current resources lack datasets explicitly annotated for ambiguity and subtypes, this gap presents an opportunity: by developing new annotated resources and exploring unsupervised approaches to ambiguity detection, we enable more robust, explainable, and human-aligned NLI systems.

  PublisherOA PDFDOI

• That Ain’t Right: Assessing LLM Performance on QA in African American and West African English Dialects

  2025-01-01

  articleOpen accessSenior author

  As Large Language Models (LLMs) gain mainstream public usage, understanding how users interact with them becomes increasingly important.Limited variety in training data raises concerns about LLM reliability across different language inputs.To explore this, we test several LLMs using functionally equivalent prompts expressed in different English dialects.We frame this analysis using Question-Answer (QA) pairs, which allow us to detect and evaluate appropriate and anomalous model behavior.We contribute a cross-LLM testing method and a new QA dataset translated into AAVE and WAPE variants.Results show a notable drop in accuracy for one dialect relative to the baseline.

  PublisherOA PDFDOI

Frequent coauthors

• David Zajic

  27 shared

• Craig S. Greenberg

  22 shared

• Marion Le Bras

  National Institute of Standards and Technology

  22 shared

• Peter Fontana

  22 shared

• Adam Dalton

  Florida Institute for Human and Machine Cognition

  22 shared

• Nizar Habash

  21 shared

• Lori Levin

  19 shared

• Brodie Mather

  Florida Institute for Human and Machine Cognition

  19 shared

Education

• Ph.D., Computer and Cognitive Science

  University of Pennsylvania

  1993

• M.S., Computer and Cognitive Science

  University of Pennsylvania

  1989

• B.A., Computer Science

  University of California, Berkeley

  1985

Awards & honors

• Fellow of the Association for Computing Machinery (ACM), 202…
• Fellow of the Association for Computational Linguistics (ACL…
• Fellow of the Association for the Advancement of Artificial…
• NSF Presidential Faculty (PECASE) Fellow, 1996
• Sloan Research Fellow