1.5k citations
- IBM (United States)US15 papers
- IBM Research - Thomas J. Watson Research CenterUS8 papers
- Massachusetts Institute of TechnologyUS5 papers
- IBM Research - AlmadenUS4 papers
- University of MichiganUS4 papers
- IBM Research - ZurichCH3 papers
- Arizona State UniversityUS2 papers
- Columbia UniversityUS2 papers
- IBM Research - HaifaIL2 papers
- IBM Research - IrelandIE2 papers
- IT University of CopenhagenDK2 papers
- Korea Advanced Institute of Science and TechnologyKR2 papers
8 papers · 1 filter
MultEval: Supporting Collaborative Alignment for LLM-as-a-Judge Evaluation Criteria
Charles Chiang, Simret Gebreegziabher, Annalisa Szymanski +6
LLM-as-a-judge approaches have emerged as a scalable solution for evaluating model behaviors, yet they rely on evaluation criteria often created by a single individual, embedding t…
Interaction Configurations and Prompt Guidance in Conversational AI for Question Answering in Human-AI Teams
Jaeyoon Song, Zahra Ashktorab, Qian Pan +3
Understanding the dynamics of human-AI interaction in question answering is crucial for enhancing collaborative efficiency. Extending from our initial formative study, which reveal…
Controlling AI Agent Participation in Group Conversations: A Human-Centered Approach
Stephanie Houde, Kristina Brimijoin, Michael Muller +6
Conversational AI agents are commonly applied within single-user, turn-taking scenarios. The interaction mechanics of these scenarios are trivial: when the user enters a message, t…
A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health
Jiawei Zhou, Amy Z. Chen, Darshi Shah +2
Recent breakthroughs in large language models (LLMs) have generated both interest and concern about their potential adoption as information sources or communication tools across di…
Towards Visualization Thumbnail Designs that Entice Reading Data-driven Articles
Hwiyeon Kim, Joohee Kim, Yunha Han +6
As online news increasingly include data journalism, there is a corresponding increase in the incorporation of visualization in article thumbnail images. However, little research e…
DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation
Bum Chul Kwon, Jungsoo Lee, Chaeyeon Chung +3
Image classification models often learn to predict a class based on irrelevant co-occurrences between input features and an output class in training data. We call the unwanted corr…