575 citations
- University of WashingtonUS32 papers
- Carnegie Mellon UniversityUS21 papers
- Allen Institute for Brain ScienceUS16 papers
- Allen Institute for Artificial IntelligenceUS11 papers
- Stanford UniversityUS7 papers
- Johns Hopkins UniversityUS5 papers
- Massachusetts Institute of TechnologyUS5 papers
- California Institute of TechnologyUS4 papers
- Google (United States)US4 papers
- Harvard UniversityUS4 papers
- University of TorontoCA4 papers
- Cold Spring Harbor LaboratoryUS3 papers
84 papers
Querying Multimodal Scientific Papers with AI: Practices and Preferences Across Blind, Low-Vision, and Sighted Scientists
Arnavi Chheda-Kothary, Lucy Lu Wang, Joseph Chee Chang +1
Visual diagrams, figures, and tables are central to scientific papers, and convey information beyond what is captured in text. While blind or low-vision (BLV) scientists have tradi…
ReFinE: Streamlining UI Mockup Iteration with Research Findings
Donghoon Shin, Bingcan Guo, Jaewook Lee +2
Although HCI research papers offer valuable design insights, designers often struggle to apply them in design workflows due to difficulties in finding relevant literature, understa…
Synthesizing Trajectory Queries from Examples
Stephen Mell, Favyen Bastani, Steve Zdancewic +1
Data scientists often need to write programs to process predictions of machine learning models, such as object detections and trajectories in video data. However, writing such quer…
LinkML: An Open Data Modeling Framework
Sierra A. T. Moxon, Harold Solbrig, Nomi L. Harris +33
Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, non-standardized spreadsheets, or data r…
Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing
Sanchaita Hazra, Doeun Lee, Bodhisattwa Prasad Majumder +1
Large Language Models have seen expanding application across domains, yet their effectiveness as assistive tools for scientific writing - an endeavor requiring precision, multimoda…
Reasoning Up the Instruction Ladder for Controllable Language Models
Zishuo Zheng, Vidhisha Balachandran, Chan Young Park +2
As large language model (LLM) based systems take on high-stakes roles in real-world decision-making, they must reconcile competing instructions from multiple sources within a singl…