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Author: 5185

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Understanding Spatiotemporal-Aware Multimodal Conversational Search in the Outdoor Urban Space

Emerging multimodal conversational search (MCS) tools (e.g., Gemini Live) allow users to search for spatiotemporal information through natural language dialogues as they move through urban space. Despite the growing popularity of these tools, there is limited understanding of how people engage with this technology. To…

Jiangnan Xu et al.Rochester Institute of Technology

Cerebra: Aligning Implicit Knowledge in Interactive SQL Authoring

LLM-driven tools have significantly lowered barriers to writing SQL queries. However, user instructions are often underspecified, assuming the model understands implicit knowledge, such as dataset schemas, domain conventions, and task-specific requirements, that isn't explicitly provided. This results in frequently er…

Yunfan Zhou et al.Zhejiang University

DiaryPlay: AI-Assisted Creation of Interactive Story Vignettes for Everyday Storytelling

An interactive vignette is a visual storytelling medium that lets the audience role-play a character and interact with non-player characters (NPCs) and the digital environment. Yet, the authoring complexity of interactive vignettes has obstructed their adoption in everyday storytelling, which builds on immediacy. We i…

Jiangnan Xu et al.Tampere University

PlanTogether: Facilitating AI Application Planning Using Information Graphs and Large Language Models

In client-AI expert collaborations, the planning stage of AI application development begins from the client; a client outlines their needs and expectations while assessing available resources (pre-collaboration planning). Despite the importance of pre-collaboration plans for discussions with AI experts for iteration a…

Dae Hyun Kim et al.Stanford University

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AINeedsPlanner: A Workbook to Support Effective Collaboration Between AI Experts and Clients

Clients often partner with AI experts to develop AI applications tailored to their needs. In these partnerships, careful planning and clear communication are critical, as inaccurate or incomplete specifications can result in misaligned model characteristics, expensive reworks, and potential friction between collaborat…

Dae Hyun Kim et al.Stanford University

A Context-Aware Onboarding Agent for Metaverse Powered by Large Language Models

One common asset of metaverse is that users can freely explore places and actions without linear procedures. Thus, it is hard yet important to understand the divergent challenges each user faces when onboarding metaverse. Our formative study (N = 16) shows that first-time users ask questions about metaverse that conce…

Jihyeong Hong et al.Korea Advanced Institute of Science and Technology

Natural Language Dataset Generation Framework for Visualizations Powered by Large Language Models

We introduce VL2NL, a Large Language Model (LLM) framework that generates rich and diverse NL datasets using Vega-Lite specifications as input, thereby streamlining the development of Natural Language Interfaces (NLIs) for data visualization. To synthesize relevant chart semantics accurately and enhance syntactic dive…

Kwon Ko et al.Korea Advanced Institute of Science and Technology

DataDive: Supporting Readers' Contextualization of Statistical Statements with Data Exploration

Statistical statements that refer to data to support narratives or claims are commonly used to inform readers about the magnitude of social issues. While contextualizing statistical statements with relevant data supports readers in building their own interpretation of statements, the complexity of finding contextual…

Hyunwoo Kim et al.Korea Advanced Institute of Science and Technology

Towards Understanding How Readers Integrate Charts and Captions: A Case Study with Line Charts

Charts often contain visually prominent features that draw attention to aspects of the data and include text captions that emphasize aspects of the data. Through a crowdsourced study, we explore how readers gather takeaways when considering charts and captions together. We first ask participants to mark visually promi…

Dae Hyun Kim et al.Stanford University

Sneak Pique: Exploring Autocompletion as a Data Discovery Scaffold for Supporting Visual Analysis

Natural language interaction has evolved as a useful modality to help users explore and interact with their data during visual analysis. Little work has been done to explore how autocompletion can help with data discovery while helping users formulate analytical questions. We developed a system called Sneak Pique as a…

Vidya Setlur et al.Tableau

Answering Questions about Charts and Generating Visual Explanations

People often use charts to analyze data, answer questions and explain their answers to others. In a formative study, we find that such human-generated questions and explanations commonly refer to visual features of charts. Based on this study, we developed an automatic chart question answering pipeline that generates…

Dae Hyun Kim et al.Stanford University

Facilitating Document Reading by Linking Text and Tables

Document authors commonly use tables to support arguments presented in the text. But, because tables are usually separate from the main body text, readers must split their attention between different parts of the document. We present an interactive document reader that automatically links document text with correspond…

Dae Hyun Kim et al.Stanford University

Paper Title	Authors	Research Topics	Paper Database	Year
Understanding Spatiotemporal-Aware Multimodal Conversational Search in the Outdoor Urban Space Emerging multimodal conversational search (MCS) tools (e.g., Gemini Live) allow users to search for spatiotemporal information through natural language dialogues as they move through urban space. Despite the growing popularity of these tools, there is limited understanding of how people engage with this technology. To…	JX Jiangnan Xu et al.Rochester Institute of Technology
Cerebra: Aligning Implicit Knowledge in Interactive SQL Authoring LLM-driven tools have significantly lowered barriers to writing SQL queries. However, user instructions are often underspecified, assuming the model understands implicit knowledge, such as dataset schemas, domain conventions, and task-specific requirements, that isn't explicitly provided. This results in frequently er…	YZ Yunfan Zhou et al.Zhejiang University
DiaryPlay: AI-Assisted Creation of Interactive Story Vignettes for Everyday Storytelling An interactive vignette is a visual storytelling medium that lets the audience role-play a character and interact with non-player characters (NPCs) and the digital environment. Yet, the authoring complexity of interactive vignettes has obstructed their adoption in everyday storytelling, which builds on immediacy. We i…	JX Jiangnan Xu et al.Tampere University
PlanTogether: Facilitating AI Application Planning Using Information Graphs and Large Language Models In client-AI expert collaborations, the planning stage of AI application development begins from the client; a client outlines their needs and expectations while assessing available resources (pre-collaboration planning). Despite the importance of pre-collaboration plans for discussions with AI experts for iteration a…	DK Dae Hyun Kim et al.Stanford University
AdRecommended Learn AI Coding at CodeNow Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work. Explore Now
AINeedsPlanner: A Workbook to Support Effective Collaboration Between AI Experts and Clients Clients often partner with AI experts to develop AI applications tailored to their needs. In these partnerships, careful planning and clear communication are critical, as inaccurate or incomplete specifications can result in misaligned model characteristics, expensive reworks, and potential friction between collaborat…	DK Dae Hyun Kim et al.Stanford University
A Context-Aware Onboarding Agent for Metaverse Powered by Large Language Models One common asset of metaverse is that users can freely explore places and actions without linear procedures. Thus, it is hard yet important to understand the divergent challenges each user faces when onboarding metaverse. Our formative study (N = 16) shows that first-time users ask questions about metaverse that conce…	JH Jihyeong Hong et al.Korea Advanced Institute of Science and Technology
Natural Language Dataset Generation Framework for Visualizations Powered by Large Language Models We introduce VL2NL, a Large Language Model (LLM) framework that generates rich and diverse NL datasets using Vega-Lite specifications as input, thereby streamlining the development of Natural Language Interfaces (NLIs) for data visualization. To synthesize relevant chart semantics accurately and enhance syntactic dive…	KK Kwon Ko et al.Korea Advanced Institute of Science and Technology
DataDive: Supporting Readers' Contextualization of Statistical Statements with Data Exploration Statistical statements that refer to data to support narratives or claims are commonly used to inform readers about the magnitude of social issues. While contextualizing statistical statements with relevant data supports readers in building their own interpretation of statements, the complexity of finding contextual…	HK Hyunwoo Kim et al.Korea Advanced Institute of Science and Technology
Towards Understanding How Readers Integrate Charts and Captions: A Case Study with Line Charts Charts often contain visually prominent features that draw attention to aspects of the data and include text captions that emphasize aspects of the data. Through a crowdsourced study, we explore how readers gather takeaways when considering charts and captions together. We first ask participants to mark visually promi…	DK Dae Hyun Kim et al.Stanford University
Sneak Pique: Exploring Autocompletion as a Data Discovery Scaffold for Supporting Visual Analysis Natural language interaction has evolved as a useful modality to help users explore and interact with their data during visual analysis. Little work has been done to explore how autocompletion can help with data discovery while helping users formulate analytical questions. We developed a system called Sneak Pique as a…	VS Vidya Setlur et al.Tableau
Answering Questions about Charts and Generating Visual Explanations People often use charts to analyze data, answer questions and explain their answers to others. In a formative study, we find that such human-generated questions and explanations commonly refer to visual features of charts. Based on this study, we developed an automatic chart question answering pipeline that generates…	DK Dae Hyun Kim et al.Stanford University
Facilitating Document Reading by Linking Text and Tables Document authors commonly use tables to support arguments presented in the text. But, because tables are usually separate from the main body text, readers must split their attention between different parts of the document. We present an interactive document reader that automatically links document text with correspond…	DK Dae Hyun Kim et al.Stanford University