Multimodal QUD: Inquisitive Questions from Scientific Figures
[project website] [dataset]
Yating Wu, William Rudman, Venkata S Govindarajan, Alexandros G. Dimakis, Junyi Jessy Li
Preprint, 2026.
ContextWeaver: Selective and Dependency-Structured Memory Construction for LLM Agents [project]
Yating Wu, Yuhao Zhang, Sayan Ghosh, Sourya Basu, Anoop Deoras, Jun Huan, Gaurav Gupta
Preprint, 2026.
†Indicates equal contribution.
QUDsim: Quantifying Discourse Similarities in LLM-Generated Text [project]
[code and data] [paper]
Ramya Namuduri, Yating Wu, Anshun Asher Zheng, Manya Wadhwa, Greg Durrett, Junyi Jessy Li
Conference on Language Modeling (COLM), 2025.
Which questions should I answer? Salience Prediction of Inquisitive Questions [project]
[code and data] [paper]
Yating Wu†, Ritika Mangla†, Alexandros G. Dimakis, Greg Durrett, Junyi Jessy Li
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024. (Oral)
Outstanding Paper Award 🏅
QUDeval: The Evaluation of Questions Under Discussion Discourse Parsing [project]
[code and data] [paper]
Yating Wu, Ritika Mangla, Greg Durrett, Junyi Jessy Li
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023. (Oral)
Elaborative Simplification as Implicit Questions Under Discussion [project]
[code and data] [paper]
Yating Wu†, William Sheffield†, Kyle Mahowald, Junyi Jessy Li
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.
Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion [project]
[code] [paper]
Wei-Jen Ko, Yating Wu, Cutter Dalton, Dananjay Srinivas, Greg Durrett, Junyi Jessy Li
Findings of the Association for Computational Linguistics (ACL), 2023.
†Indicates equal contribution.
Modeling Bilingual Disfluencies with Large Language Models [project]
Negin Raoof†, Yating Wu†, Carlos Bonilla†, Junyi Jessy Li, Stephanie M Grasso, Alexandros G. Dimakis, Zoi Gkalitsiou
ICML Workshop on LLMs and Cognition, 2024.
A parser connects each sentence to an earlier sentence through the implicit question it answers.
Connection to QUDeval QUDeval develops a human evaluation protocol for QUD parsing and examines the questions produced by parsing systems. Abstract ↗
Connection to QSalience QUD parsing recovers the questions a text answers. QSalience asks which questions would most help a reader understand that text. Section 2 ↗
Four linguistic criteria test whether a generated question fits its anchor, answer, and preceding text.
Connection to QUD dependency parsing QUDeval develops a human evaluation protocol for QUD parsing and examines the questions produced by parsing systems. Abstract ↗
Connection to QUDsim QUDsim cites QUDeval in its question generation procedure, then uses answerability across documents to measure discourse similarity. Section 4 ↗
Connection to QSalience QSalience follows QUDeval’s constraint that a question must be grounded in its anchor sentence when judging question validity. Section 3 ↗
The questions answered by two documents reveal similarities in how they develop their ideas.
Connection to QUDeval QUDsim cites QUDeval in its question generation procedure, then uses answerability across documents to measure discourse similarity. Section 4 ↗
Implicit questions describe the explanations editors add when simplifying an article.
Connection to QSalience QSalience cites ElabQUD to motivate choosing which concepts need an explanation, beyond generating possible elaborations. Section 2 ↗
We predict how much answering a question would help a reader understand the preceding text.
Connection to QUD dependency parsing QUD parsing recovers the questions a text answers. QSalience asks which questions would most help a reader understand that text. Section 2 ↗
Connection to QUDeval QSalience follows QUDeval’s constraint that a question must be grounded in its anchor sentence when judging question validity. Section 3 ↗
Connection to ElabQUD QSalience cites ElabQUD to motivate choosing which concepts need an explanation, beyond generating possible elaborations. Section 2 ↗
Connection to MQUD MQUD draws on QSalience when extending question salience from news text to scientific figures and a paper’s research goals. Section 2 ↗
Scientific figures raise questions about a paper’s argument. MQUD studies those questions with annotations from the papers’ authors.
Connection to QSalience MQUD draws on QSalience when extending question salience from news text to scientific figures and a paper’s research goals. Section 2 ↗
Language model surprisal and linguistic features help us study disfluencies in monolingual and bilingual speech.
Dependencies between an agent’s reasoning steps guide which observations and decisions to retain in context.