activity
20242026
most citedNormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models

10 citations · 16 across the 6 of their papers we have counts for

collaborators

6 papers

cs.HC2026

AI-Generated Email Drafts Shift Culturally Distinctive Communication Styles in Professional Email

Shintaro Sakai, Alice Gao, Yuichi Shoda +1

AI assistants that support email composition may shift cultural communication norms, such as the directness typical of low-context cultures like the US versus the indirectness and…

cs.HC2026

Framing an AI with Values Reduces AI Reliance in AI-supported Writing Tasks

Alice Gao, Andrew N. Meltzoff, Maarten Sap +1

Despite a global user base adopting large language models (LLMs) for daily writing tasks, model suggestions tend to align with Western values. Research has shown users commonly acc…

cs.CL2026

Training Computer Use Agents to Assess the Usability of Graphical User Interfaces

Alice Gao, Weixi Tong, Rishab Vempati +4

Usability testing with experts and potential users can assess the effectiveness, efficiency, and user satisfaction of graphical user interfaces (GUIs) but doing so remains a costly…

cs.HC2026

Language Scent: Exploring Cross-Language Information Navigation

Jiawen Stefanie Zhu, Katharina Reinecke, Tanushree Mitra

While multilingual users often switch between languages when seeking information, this process remains undersupported by current systems where information is typically siloed by la…

cs.HC2025★ 6 cited

Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs

Jeffrey Basoah, Daniel Chechelnitsky, Tao Long +5

As large language models (LLMs) increasingly adapt and personalize to diverse sets of users, there is an increased risk of systems appropriating sociolects, i.e., language styles o…

cs.CL2024★ 10 cited

NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models

Abhinav Rao, Akhila Yerukola, Vishwa Shah +2

To be effectively and safely deployed to global user populations, large language models (LLMs) may need to adapt outputs to user values and cultures, not just know about them. We i…