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版本更新 · v2arXiv:2606.10177

VArify: A Visual Analytics System for Verifying Knowledge Enhanced Large Language Model Responses in Food Science

Sam Yu-Te Lee, Yan To Linus Lam, Manami Nakagawa, Kwan-Liu Ma

原文摘要

Graph Retrieval-Augmented Generation (GraphRAG) grounds Large Language Model (LLM) responses in structured, domain-specific knowledge. However, verification remains difficult because errors can arise at multiple layers: an answer may not be faithful to its retrieved context, the retrieval pipeline may select irrelevant evidence, and the knowledge graph may omit relevant facts or contain incorrect triplets. We present VArify, a visual analytics system for food-science researchers to inspect and repair these layers in a GraphRAG workflow. A formative study with five participants using a conventional chat-and-table interface revealed behaviors reflecting answer anchoring, over-trust, and difficulty tracing multi-hop relationships. In response, VArify combines an evidence-first answer reveal with coordinated chat and hierarchical graph views, provenance inspection, and subquery editing. It connects the generated answer, the retrieval state, and the provenance-linked graph so that researchers can trace discrepancies across them and repair the retrieved context. From a qualitative system-use study on six food science researchers, we characterize how they scrutinize GraphRAG evidence with progressive visual inspection. Our study also reveals an important verification boundary: information absent from both retrieval and a user's knowledge remains unverifiable. We discuss the broader role of visualization in supporting oversight of LLM responses and outline future research directions.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.11954

VizCopilot: Fostering Appropriate Reliance on Enterprise Chatbots with Context Visualization

Sam Yu-Te Lee, Jingya Chen, Albert Calzaretto, Richard Lee, Samir Passi, Alice Ferng, Mihaela Vorvoreanu

原文摘要

Enterprise AI chatbots show promise in supporting knowledge workers in information synthesis tasks by retrieving context from large, heterogeneous databases before generating answers. However, when the retrieved context misaligns with user intentions, these chatbots often produce ``irrelevantly right'' responses that raise the risk of overreliance. To foster appropriate reliance, we need a different approach than asking users to verify chatbot outputs. We posit that human oversight and modification of the context retrieval process can improve the quality of chatbots' outputs and users' awareness, resulting in more appropriate reliance and higher accountability. In this work, we explore this approach through a Research-through-Design study using VizCopilot, a chatbot extended with corpus visualization techniques. Through a within-subject study comparing VizCopilot and a simplified pure-text chatbot, we evaluate the role of visualization in enterprise chatbots and surface future design opportunities for fostering appropriate reliance through context engineering. Our findings show that visualization not only helps users detect and correct misaligned context, but also enhances user agency and system transparency. At the same time, the study reveals limitations regarding verification support and trust in AI summaries. Based on these findings, we discuss how visualization can foster appropriate reliance, the associated design trade-offs, and our vision towards transparent and sustainable human-AI interaction.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.33997

Detecting and Understanding Ambiguity in Visualizations with Role-Playing Vision Language Model Interpretations

Garrick Chan, Yun-Hsin Kuo, Sam Yu-Te Lee, Yinuo Tang, Kwan-Liu Ma

原文摘要

Communicative visualizations can support multiple chart-grounded takeaways, including divergent or conflicting ones; we refer to this variation as interpretive ambiguity. Such ambiguity can undermine a visualization's communicative goal when the supported takeaways diverge from its intended message. Evaluating visualizations with target audiences can surface this variation, but such evaluations can be costly and difficult to scale. In this work, we explore persona-conditioned Vision Language Models (VLMs) as a complementary evaluation tool for interpretive ambiguity. For a given visualization, our pipeline uses topic-relevant personas to elicit candidate contextual takeaways supported by interpretations of perceived visual patterns, such as trends and clusters. We derive a Target--Pattern--Scope framework for comparing these interpretations, use LLM judges to assign Compatible, Disjoint, or Opposing ambiguity labels to statement pairs, and summarize their distribution in an ambiguity profile. To improve chart grounding, we benchmark candidate VLMs on chart comprehension and select the highest-performing model for each chart type. We evaluate statement-pair labeling against human-authored annotations and apply the approach to real-world, public-facing visualizations curated from established datasets. Through case studies, we examine how perceived visual patterns relate to contextual takeaways and how a visualization redesign changes the sampled interpretations. Our findings show how persona-conditioned VLMs can complement human feedback by generating candidate contextual takeaways and surfacing potential ambiguity for further inspection.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16245

Agentic Dashboards: A New Design Paradigm for Visual Analytics

Sam Yu-Te Lee, Yun-Hsin Kuo, Kwan-Liu Ma

原文摘要

Visual analytics systems traditionally combine analytical methods and interactive visualizations into a fixed application specified at the design phase. Coding agents offer a flexible approach: experts could conduct analysis through natural-language instructions. We envision an agentic dashboard as a persistent visual analytics workspace where an agent makes decisions about analytical methods, views, and their coordination within a designer-defined decision space. This approach shifts part of the visualization designer's role from engineering a fixed interface to defining the decisions an agent may make at runtime. We illustrate how such a dashboard could operate with current capabilities and propose criteria for evaluating its behavior and potential failures. As analytical workflows become more fluid and agent-mediated, visualization can help experts maintain awareness, coordinate with agents, and exercise judgment.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.08914

Vibe Visualizing: How Visualization Novices Try (and Fail) to Generate and Interpret Visualizations with Conversational AI

Sam Yu-Te Lee, Yun-Hsin Kuo, Chifang Chou, Matthew Ward, Xiwei Xuan, Kwan-Liu Ma

原文摘要

Conversational AI lowers technical barriers to generating and interpreting visualizations. People without visualization training or practical experience can therefore perform these tasks, but may lack the expertise needed to evaluate the resulting charts and interpretations. Through a study with 20 visualization novices, we examine conversation logs from 60 sessions with ChatGPT and develop a codebook that characterizes participant and AI behaviors in AI-assisted visualization. Paired with interview transcripts and survey responses, we derive three themes under outcome quality, AI non-compliance, and trust, verification, and repair. An exploratory role analysis identifies two session-level roles distinguished by user initiative and associated with different failure patterns. A comparative analysis with Gemini and Claude further reveals distinct system-specific behaviors. Based on these findings, we derive five design recommendations for AI-assisted visualization design and discuss implications for visualization literacy, human-AI roles, accountability, and agentic visualization.

cs.HC

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arXiv ↗PDF ↗