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20242026
most citedTo Rely or Not to Rely? Evaluating Interventions for Appropriate Reliance on Large Language Models

1 citations · 1 across the 8 of their papers we have counts for

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cs.HC2026

Large Language Lovers: Lived Experiences of Negotiating Agency and Platform Control in AI Companionship

Patrick Yung Kang Lee, Jessica Y. Bo, Zixin Zhao +6

Individuals are turning to increasingly anthropomorphic, general-purpose chatbots for AI companionship, rather than roleplay-specific platforms. However, not much is known about ho…

cs.HC2025

Invisible Saboteurs: Sycophantic LLMs Mislead Novices in Problem-Solving Tasks

Jessica Y. Bo, Majeed Kazemitabaar, Mengqing Deng +2

Sycophancy, the tendency of LLM-based chatbots to express excessive agreement with their users, even when inappropriate, is emerging as a significant risk in human-AI interactions.…

cs.HC2025

Who's the Leader? Analyzing Novice Workflows in LLM-Assisted Debugging of Machine Learning Code

Jessica Y. Bo, Majeed Kazemitabaar, Emma Zhuang +1

While LLMs are often touted as tools for democratizing specialized knowledge to beginners, their actual effectiveness for improving task performance and learning is still an open q…

cs.HC2025

Steerable Chatbots: Exploring Personalization Control Interfaces via LLM Activation Steering

Jessica Y. Bo, Tianyu Xu, Ishan Chatterjee +3

Personalizing LLM responses typically requires users to articulate their preferences through prompting, which can be burdensome at cold start and difficult to articulate in natural…

cs.HC2024★ 1 cited

To Rely or Not to Rely? Evaluating Interventions for Appropriate Reliance on Large Language Models

Jessica Y. Bo, Sophia Wan, Ashton Anderson

As Large Language Models become integral to decision-making, optimism about their power is tempered with concern over their errors. Users may over-rely on LLM advice that is confid…

cs.HC2024

Incremental XAI: Memorable Understanding of AI with Incremental Explanations

Jessica Y. Bo, Pan Hao, Brian Y. Lim

Many explainable AI (XAI) techniques strive for interpretability by providing concise salient information, such as sparse linear factors. However, users either only see inaccurate…