30 citations · 43 across the 21 of their papers we have counts for
21 papers
From Small Talk to Rapport: Exploring Robot Self-Disclosure in Collaborative Tasks
Kaitlynn Taylor Pineda, Anvii Mishra, Brian Chien +4
People naturally chat while collaborating and share personal information (i.e., self-disclose) to build rapport and maintain social connections. As robots are increasingly develope…
Informal Learning Emerges in Everyday Human-LLM Interaction
Zixin Chen, Haotian Li, Ziang Xiao +2
As LLMs become increasingly capable of completing tasks for users, a central concern is that everyday AI use may become primarily cognitive offloading, eroding the opportunities th…
Can We Trust Item Response Theory for AI Evaluation?
Han Jiang, Sunbeom Kwon, Jinwen Luo +2
AI benchmarks increasingly leverage item-level statistical models, particularly item response theory (IRT), to estimate capabilities, rank systems, select informative examples, and…
AI-Mediated Negotiation: Design Reflections and Lessons
Veda Duddu, Jash Rajesh Parekh, Andy Mao +4
Conversational AI promises a new kind of preparation for high-stakes workplace negotiations -- personalized, interactive, and capable of simulating realistic resistance. That promi…
On Defining Erasure Harms for NLP
Yu Lu Liu, Arnav Goel, Jackie Chi Kit Cheung +3
The deployment of NLP systems has raised concerns about harms they might produce, including representational harms. Recent literature has begun to conceptualize and measure one suc…
AI Evaluation Should Require Standardized Item-Level Data Releases
Han Jiang, Susu Zhang, Dongyao Zhu +6
This position paper argues that standardized item-level benchmark data should become the default infrastructure for AI evaluation. Current evaluations suffer from underspecified it…
What Makes a Good Response? An Empirical Analysis of Quality in Qualitative Interviews
Jonathan Ivey, Anjalie Field, Ziang Xiao
Qualitative interviews provide essential insights into human experiences when they elicit high-quality responses. While qualitative and NLP researchers have proposed various measur…
Not My Truce: Personality Differences in AI-Mediated Workplace Negotiation
Veda Duddu, Jash Rajesh Parekh, Andy Mao +4
AI-driven conversational coaching is increasingly used to support workplace negotiation, yet prior work assumes uniform effectiveness across users. We challenge this assumption by…
Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents
Nikhil Sharma, Zheng Zhang, Daniel Lee +4
High-quality feedback is essential for effective human-AI interaction. It bridges knowledge gaps, corrects digressions, and shapes system behavior; both during interaction and thro…
Population-Aligned Persona Generation for LLM-based Social Simulation
Zhengyu Hu, Jianxun Lian, Zheyuan Xiao +7
Recent advances in large language models (LLMs) have enabled human-like social simulations at unprecedented scale and fidelity, offering new opportunities for computational social…
Does AI Coaching Prepare us for Workplace Negotiations?
Veda Duddu, Jash Rajesh Parekh, Andy Mao +4
Workplace negotiations are undermined by psychological barriers, which can even derail well-prepared tactics. AI offers personalized and always -- available negotiation coaching, y…
From Queries to Criteria: Understanding How Astronomers Evaluate LLMs
Alina Hyk, Kiera McCormick, Mian Zhong +7
There is growing interest in leveraging LLMs to aid in astronomy and other scientific research, but benchmarks for LLM evaluation in general have not kept pace with the increasingl…
The Incomplete Bridge: How AI Research (Mis)Engages with Psychology
Han Jiang, Pengda Wang, Xiaoyuan Yi +2
Social sciences have accumulated a rich body of theories and methodologies for investigating the human mind and behaviors, while offering valuable insights into the design and unde…
PICACO: Pluralistic In-Context Value Alignment of LLMs via Total Correlation Optimization
Han Jiang, Dongyao Zhu, Xiaoyuan Yi +3
In-Context Learning has shown great potential for aligning Large Language Models (LLMs) with human values, helping reduce harmful outputs and accommodate diverse preferences withou…
From Text to Trust: Empowering AI-assisted Decision Making with Adaptive LLM-powered Analysis
Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu +2
AI-assisted decision making becomes increasingly prevalent, yet individuals often fail to utilize AI-based decision aids appropriately especially when the AI explanations are absen…
Assistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Support
Kevin Pu, Daniel Lazaro, Ian Arawjo +4
AI programming tools enable powerful code generation, and recent prototypes attempt to reduce user effort with proactive AI agents, but their impact on programming workflows remain…
Generative Personality Simulation via Theory-Informed Structured Interview
Pengda Wang, Huiqi Zou, Han Jiang +5
Despite their potential as human proxies, LLMs often fail to generate heterogeneous data with human-like diversity, thereby diminishing their value in advancing social science rese…
Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review
Rock Yuren Pang, Hope Schroeder, Kynnedy Simone Smith +4
Large language models (LLMs) have been positioned to revolutionize HCI, by reshaping not only the interfaces, design patterns, and sociotechnical systems that we study, but also th…
Interruption Handling for Conversational Robots
Shiye Cao, Jiwon Moon, Amama Mahmood +4
Interruptions, a fundamental component of human communication, can enhance the dynamism and effectiveness of conversations, but only when effectively managed by all parties involve…
Can LLM "Self-report"?: Evaluating the Validity of Self-report Scales in Measuring Personality Design in LLM-based Chatbots
Huiqi Zou, Pengda Wang, Zihan Yan +2
A chatbot's personality design is key to interaction quality. As chatbots evolved from rule-based systems to those powered by large language models (LLMs), evaluating the effective…
Improving Context-Aware Preference Modeling for Language Models
Silviu Pitis, Ziang Xiao, Nicolas Le Roux +1
While finetuning language models from pairwise preferences has proven remarkably effective, the underspecified nature of natural language presents critical challenges. Direct prefe…