Tongshuang Wu

schoolCarnegie Mellon Universitybar_chart#1455 Global Rank

Total Papers

HCI Rank

#1455

Research Areas

Years Active

Recent Publications

Not Everyone Wins with LLMs: Behavioral Patterns and Pedagogical Implications for AI Literacy in Programmatic Data Science

LLMs promise to democratize technical work in complex domains like programmatic data analysis, but not everyone benefits equally. We study how students with varied experiences use LLMs to complete Python-based data analysis in computational notebooks in a graduate course. Drawing on homework logs, recordings, and surv…

CHI '264th AuthorHonorable MentionMental Health Apps & Online Support Communities +2

Evidotes: Integrating Scientific Evidence and Anecdotes to Support Uncertainties Triggered by Peer Health Posts

Peer health posts surface new uncertainties, such as questions and concerns for readers. Prior work focused primarily on improving relevance and accuracy fails to address users' diverse information needs and emotions triggered. Instead, we propose directly addressing these by information augmentation. We introduce Evi…

CHI '265th+ AuthorHuman-LLM Collaboration +2

Behavioral Indicators of Overreliance During Interaction with Conversational Language Models

LLMs are now embedded in a wide range of everyday scenarios. However, their inherent hallucinations risk hiding misinformation in fluent responses, raising concerns about overreliance on AI. Detecting overreliance is challenging, as it often arises in complex, dynamic contexts and cannot be easily captured by post-hoc…

IUI '25CorrespondingExplainable AI (XAI) +1

Orbit: A Framework for Designing and Evaluating Multi-objective Rankers

Machine learning in production needs to balance multiple objectives: This is particularly evident in ranking or recommendation models, where conflicting objectives such as user engagement, satisfaction, diversity, and novelty must be considered at the same time. However, designing multi-objective rankers is inherently…

CHI '242nd AuthorHuman-LLM Collaboration +2

Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Models

Sensemaking in unfamiliar domains can be challenging, demanding considerable user effort to compare different options with respect to various criteria. Prior research and our formative study found that people would benefit from reading an overview of an information space upfront, including the criteria others previous…

CHI '245th+ AuthorCommunity Collaboration & Wikipedia +1

Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia

AI tools are increasingly deployed in community contexts. However, datasets used to evaluate AI are typically created by developers and annotators outside a given community, which can yield misleading conclusions about AI performance. How might we empower communities to drive the intentional design and curation of eva…

UIST '232nd AuthorHuman-LLM Collaboration +2

Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking

Efficiently reviewing scholarly literature and synthesizing prior art are crucial for scientific progress. Yet, the growing scale of publications and the burden of knowledge make synthesis of research threads more challenging than ever. While significant research has been devoted to helping scholars interact with in…

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