21 citations · 64 across the 20 of their papers we have counts for
23 papers
Learning Who Benefits Most: Multi-Treatment Causal Ranking for Population Health Stratification
Rocco Zaccagnino, Martina Girolamo, Alberto Lombardi +3
Population health stratification frameworks, such as the Italian DM77, organize patients by clinical need and care pathways but offer limited guidance on which care changes to prio…
Simple Diffusion Language Models Are More Effective Few-Step Generators Than Reported
Hasan Amin, Ming Yin, Rajiv Khanna
Diffusion language models (DLMs) promise fast parallel generation, yet high-quality samples often require large number of refinement steps, which diminishes their advantage in prac…
Graphionale: How Graph Visualizations of LLM Rationales Affect Human Decision Making
Xinru Wang, Zhexuan Ma, Ming Yin +2
Large Language Models (LLMs) are increasingly equipped with augmented reasoning capabilities to generate rationales that support human decision-making. Yet these text-dense rationa…
From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives?
Hasan Amin, Harry Yizhou Tian, Xiaoni Duan +3
Although large language models (LLMs) are increasingly used as annotators at scale, they are typically treated as a pragmatic fallback rather than a faithful estimator of human per…
Understanding the Effects of AI-Assisted Critical Thinking on Human-AI Decision Making
Harry Yizhou Tian, Hasan Amin, Ming Yin
Despite the growing prevalence of human-AI decision making, the human-AI team's decision performance often remains suboptimal, partially due to insufficient examination of humans'…
Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration
Hasan Amin, Ming Yin, Rajiv Khanna
In human-AI decision making, designing AI that complements human expertise has been a natural strategy to enhance human-AI collaboration, yet it often comes at the cost of decrease…
Human-LLM Collaborative Feature Engineering for Tabular Data
Zhuoyan Li, Aditya Bansal, Jinzhao Li +8
Large language models (LLMs) are increasingly used to automate feature engineering in tabular learning. Given task-specific information, LLMs can propose diverse feature transforma…
Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs
Shasha Zhou, Mingyu Huang, Jack Cole +4
The recent proliferation of large language models (LLMs) holds the potential to revolutionize healthcare, with strong capabilities in diverse medical tasks. Yet, deploying LLMs in…
XAgen: An Explainability Tool for Identifying and Correcting Failures in Multi-Agent Workflows
Xinru Wang, Ming Yin, Eunyee Koh +1
As multi-agent systems powered by Large Language Models (LLMs) are increasingly adopted in real-world workflows, users with diverse technical backgrounds are now building and refin…
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…
Utilizing Human Behavior Modeling to Manipulate Explanations in AI-Assisted Decision Making: The Good, the Bad, and the Scary
Zhuoyan Li, Ming Yin
Recent advances in AI models have increased the integration of AI-based decision aids into the human decision making process. To fully unlock the potential of AI-assisted decision…
How Does the Disclosure of AI Assistance Affect the Perceptions of Writing?
Zhuoyan Li, Chen Liang, Jing Peng +1
Recent advances in generative AI technologies like large language models have boosted the incorporation of AI assistance in writing workflows, leading to the rise of a new paradigm…
The Value, Benefits, and Concerns of Generative AI-Powered Assistance in Writing
Zhuoyan Li, Chen Liang, Jing Peng +1
Recent advances in generative AI technologies like large language models raise both excitement and concerns about the future of human-AI co-creation in writing. To unpack people's…
"Are You Really Sure?" Understanding the Effects of Human Self-Confidence Calibration in AI-Assisted Decision Making
Shuai Ma, Xinru Wang, Ying Lei +3
In AI-assisted decision-making, it is crucial but challenging for humans to achieve appropriate reliance on AI. This paper approaches this problem from a human-centered perspective…
Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making
Shuai Ma, Chenyi Zhang, Xinru Wang +2
Artificial Intelligence (AI) is increasingly employed in various decision-making tasks, typically as a Recommender, providing recommendations that the AI deems correct. However, re…
Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making
Shuai Ma, Qiaoyi Chen, Xinru Wang +4
In AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole. In such a paradigm, humans are found to rarely t…
Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making
Zhuoran Lu, Dakuo Wang, Ming Yin
AI assistance in decision-making has become popular, yet people's inappropriate reliance on AI often leads to unsatisfactory human-AI collaboration performance. In this paper, thro…
Decoding AI's Nudge: A Unified Framework to Predict Human Behavior in AI-assisted Decision Making
Zhuoyan Li, Zhuoran Lu, Ming Yin
With the rapid development of AI-based decision aids, different forms of AI assistance have been increasingly integrated into the human decision making processes. To best support h…
Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations
Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu +1
The collection and curation of high-quality training data is crucial for developing text classification models with superior performance, but it is often associated with significan…
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections
Maria Leonor Pacheco, Tunazzina Islam, Lyle Ungar +2
Experts across diverse disciplines are often interested in making sense of large text collections. Traditionally, this challenge is approached either by noisy unsupervised techniqu…
Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making
Shuai Ma, Ying Lei, Xinru Wang +4
In AI-assisted decision-making, it is critical for human decision-makers to know when to trust AI and when to trust themselves. However, prior studies calibrated human trust only b…
A Holistic Framework for Analyzing the COVID-19 Vaccine Debate
Maria Leonor Pacheco, Tunazzina Islam, Monal Mahajan +4
The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions. Combating the outcomes of this infodemic is not only a question of identifyi…
Working in Pairs: Understanding the Effects of Worker Interactions in Crowdwork
Chien-Ju Ho, Ming Yin
Crowdsourcing has gained popularity as a tool to harness human brain power to help solve problems that are difficult for computers. Previous work in crowdsourcing often assumes tha…