ClarVis: A Dataset of Clarification Requests and Grounding in Collaborative Data Visualization Dialogues
Year: 2026
Type: conference-paper
Abstract: Clarification Requests (CRs) play a crucial role in human communication.However, existing datasets are often limited to single-turn clarifications or simulated tasks.We present a novel task-oriented dialogue dataset, ClarVis, of CRs collected from real-time multi-user collaborative data-exploration sessions, where users analyze data together while interacting with a visualization-generating conversational assistant.This dataset fills important gaps in current CR datasets by identifying naturally occurring CRs grounded by real-world modalities like hearing, vision, and actions in the physical environment.Our dataset includes 6.3K utterances across collaborative tasks, where each CR is annotated with a grounding modality -Auditory (A), Visual (V), or Kinesthetic (K)-that captures the context the clarification pertains to.Further, we establish benchmark tasks for CR identification and grounding-modality classification, and evaluate them with traditional machine learning models as well as instructiontuned large language models.The results highlight both the learnability and the difficulty of these tasks, and position ClarVis as a useful resource for studying clarifications in collaborative dialogue settings.
Authors: Abari Bhattacharya, Barbara Di Eugenio
Institution: University of Illinois Chicago
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7213996471