Tongshuang Wu

schoolUniversity Of Washingtonbar_chart#1666 Global Rank

Total Papers

HCI Rank

#1666

Research Areas

Years Active

Recent Publications

AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts

Although large language models (LLMs) have demonstrated impressive potential on simple tasks, their breadth of scope, lack of transparency, and insufficient controllability can make them less effective when assisting humans on more complex tasks. In response, we introduce the concept of Chaining LLM steps together, wh…

CHI '225th+ AuthorParticipatory Design +1

StoryBuddy: A Human-AI Collaborative Agent for Parent-Child Interactive Storytelling with Flexible Parent Involvement

Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions. While recent advances made AI generation of…

CHI '222nd AuthorHonorable MentionAlgorithmic Fairness & Bias +2

Pretty Princess vs. Successful Leader: Gender Roles in Greeting Card Messages

People write personalized greeting cards on various occasions. While prior work has studied gender roles in greeting card messages, systematic analysis at scale and tools for raising the awareness of gender stereotyping remain under-investigated. To this end, we collect a large greeting card message corpus covering th…

CHI '212nd AuthorExplainable AI (XAI) +1

Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance

Many researchers motivate explainable AI with studies showing that human-AI team performance on decision-making tasks improves when the AI explains its recommendations. However, prior studies observed improvements from explanations only when the AI, alone, outperformed both the human and the best team. Can explanation…

CHI '204th AuthorExplainable AI (XAI) +2

No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML

Automatically generated explanations of how machine learning (ML) models reason can help users understand and accept them. However, explanations can have unintended consequences: promoting over-reliance or undermining trust. This paper investigates how explanations shape users' perceptions of ML models with or without…

CHI '201st AuthorInteractive Data Visualization +2

Tempura: Query Analysis with Structural Templates

Analyzing queries from search engines and intelligent assistants is difficult. A key challenge is organizing queries into interpretable, context-preserving, representative, and flexible groups. We present structural templates, abstract queries that replace tokens with their linguistic feature forms, as a query groupin…

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