6 papers
Why LLMs Give In: Conversational Factors and Reasoning Behind Medical Sycophancy
Kaike Ping, Buse Ãarık, Caleb Wohn +3
A language model that abandons a correct medical answer under user pushback is more dangerous than one that was simply wrong, because it lends the credibility of a correct answer t…
"Are we writing an advice column for Spock here?" Understanding Stereotypes in AI Advice for Autistic Users
Caleb Wohn, Buse Ãarık, Xiaohan Ding +3
Autistic individuals sometimes disclose autism when asking LLMs for social advice, hoping for more personalized responses. However, they also recognize that these systems may repro…
A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse
Xiaohan Ding, Kaike Ping, Buse Ãarık +1
Understanding causal language in informal discourse is a core yet underexplored challenge in NLP. Existing datasets largely focus on explicit causality in structured text, providin…
Designing Human-AI Collaboration to Support Learning in Counterspeech Writing
Xiaohan Ding, Kaike Ping, Uma Sushmitha Gunturi +7
Online hate speech has become increasingly prevalent on social media, causing harm to individuals and society. While automated content moderation has received considerable attentio…
How Managers Perceive AI-Assisted Conversational Training for Workplace Communication
Lance T. Wilhelm, Xiaohan Ding, Kirk McInnis Knutsen +2
Effective workplace communication is essential for managerial success, yet many managers lack access to tailored and sustained training. Although AI-assisted communication systems…
Reimagining Support: Exploring Autistic Individuals' Visions for AI in Coping with Negative Self-Talk
Buse Carik, Victoria Izaac, Xiaohan Ding +2
Autistic individuals often experience negative self-talk (NST), leading to increased anxiety and depression. While therapy is recommended, it presents challenges for many autistic…