70 citations · 83 across the 31 of their papers we have counts for
15 papers · 1 filter
Adaptive Testing for LLM Evaluation: A Psychometric Alternative to Static Benchmarks
Peiyu Li, Xiuxiu Tang, Si Chen +4
Evaluating large language models (LLMs) typically requires thousands of benchmark items, making the process expensive, slow, and increasingly impractical at scale. Existing evaluat…
Graph Diffusion Transformers are In-Context Molecular Designers
Gang Liu, Jie Chen, Yihan Zhu +4
In-context learning allows large models to adapt to new tasks from a few demonstrations, but it has shown limited success in molecular design. Existing databases such as ChEMBL con…
KEO: Knowledge Extraction on OMIn via Knowledge Graphs and RAG for Safety-Critical Aviation Maintenance
Kuangshi Ai, Jonathan A. Karr, Meng Jiang +2
We present Knowledge Extraction on OMIn (KEO), a domain-specific knowledge extraction and reasoning framework with large language models (LLMs) in safety-critical contexts. Using t…
AI Academy: Building Generative AI Literacy in Higher Ed Instructors
Si Chen, Xiuxiu Tang, Alison Cheng +3
Generative AI is reshaping higher education, yet research has focused largely on students, while instructors remain understudied despite their central role in mediating adoption an…
Exploring Conversational Design Choices in LLMs for Pedagogical Purposes: Socratic and Narrative Approaches for Improving Instructor's Teaching Practice
Si Chen, Isabel R. Molnar, Peiyu Li +5
Large language models (LLMs) typically generate direct answers, yet they are increasingly used as learning tools. Studying instructors' usage is critical, given their role in teach…
Racing to the Starting Line: Measuring Opportunity and Improvement in Cross Country
Jonathan A. Karr, Ryan M. Fryer, Nitesh V. Chawla
Collegiate cross country programs set race schedules without large comparable datasets across courses and conditions. Using the comprehensive-era National Running Club Database (NR…
LatentPrompt: Optimizing Promts in Latent Space
Mateusz Bystroński, Grzegorz Piotrowski, Nitesh V. Chawla +1
Recent advances have shown that optimizing prompts for Large Language Models (LLMs) can significantly improve task performance, yet many optimization techniques rely on heuristics…
Understanding Student Attitudes and Acceptability of GenAI Tools in Higher Ed: Scale Development and Evaluation
Xiuxiu Tang, Si Chen, Ying Cheng +3
As generative AI (GenAI) tools like ChatGPT become more common in higher education, understanding student attitudes is essential for evaluating their educational impact and support…
"Not in My Backyard": LLMs Uncover Online and Offline Social Biases Against Homelessness
Jonathan A. Karr, Benjamin F. Herbst, Matthew L. Sisk +5
Homelessness is a persistent social challenge, impacting millions worldwide. Over 876,000 people experiencing homelessness (PEH) were recorded in the U.S. in 2025. Social bias is a…
Building Scaffolding Dialogue Data with LLM-Simulated Novices
Si Chen, Izzy Molnar, Ting Hua +6
High-quality, multi-turn instructional dialogues between novices and experts are essential for developing AI systems that support teaching, learning, and decision-making. These dia…
FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE
Khiem Le, Tuan Tran, Ting Hua +1
Existing resource-adaptive LoRA federated fine-tuning methods enable clients to fine-tune models using compressed versions of global LoRA matrices, in order to accommodate various…
Graph Foundation Models: A Comprehensive Survey
Zehong Wang, Zheyuan Liu, Tianyi Ma +16
Graph-structured data pervades domains such as social networks, biological systems, knowledge graphs, and recommender systems. While foundation models have transformed natural lang…
SMOTExT: SMOTE meets Large Language Models
Mateusz Bystroński, Mikołaj Hołysz, Grzegorz Piotrowski +2
Data scarcity and class imbalance are persistent challenges in training robust NLP models, especially in specialized domains or low-resource settings. We propose a novel technique,…
Bridging the AI Adoption Gap: Designing an Interactive Pedagogical Agent for Higher Education Instructors
Si Chen, Reid Metoyer, Khiem Le +6
Instructors play a pivotal role in integrating AI into education, yet their adoption of AI-powered tools remains inconsistent. Despite this, limited research explores how to design…
Beyond Message Passing: Neural Graph Pattern Machine
Zehong Wang, Zheyuan Zhang, Tianyi Ma +3
Graph learning tasks often hinge on identifying key substructure patterns -- such as triadic closures in social networks or benzene rings in molecular graphs -- that underpin downs…