most citedEpistemic Alignment: A Mediating Framework for User-LLM Knowledge Delivery

1 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL2025

Who's Asking? Simulating Role-Based Questions for Conversational AI Evaluation

Navreet Kaur, Hoda Ayad, Hayoung Jung +3

Language model users often embed personal and social context in their questions. The asker's role -- implicit in how the question is framed -- creates specific needs for an appropr…

cs.CL20251 cited

ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios

Mahika Phutane, Hayoung Jung, Matthew Kim +2

Large language models (LLMs) are increasingly under scrutiny for perpetuating identity-based discrimination in high-stakes domains such as hiring, particularly against people with…

cs.CY2025

MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform

Hayoung Jung, Shravika Mittal, Ananya Aatreya +3

Understanding the prevalence of misinformation in health topics online can inform public health policies and interventions. However, measuring such misinformation at scale remains…

cs.HC20251 cited

Epistemic Alignment: A Mediating Framework for User-LLM Knowledge Delivery

Nicholas Clark, Hua Shen, Bill Howe +1

LLMs increasingly serve as tools for knowledge acquisition, yet users cannot effectively specify how they want information presented. When users request that LLMs "cite reputable s…

cs.HC2025

Mind the Value-Action Gap: Do LLMs Act in Alignment with Their Values?

Hua Shen, Nicholas Clark, Tanushree Mitra

Existing research primarily evaluates the values of LLMs by examining their stated inclinations towards specific values. However, the "Value-Action Gap," a phenomenon rooted in env…