1 citations · 2 across the 14 of their papers we have counts for
15 papers
Does Thinking Help Fairness? Reasoning Tokens Resolve Some Biases but Create More
Deng Pan, Joe Germino, Yihong Ma +4
Thinking in reasoning language models (RLMs) has been subject to debate on whether it resolves or amplifies bias. Prior works have shown competing conclusions in both directions. U…
TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors
Isabel Molnar, Peiyu Li, Si Chen +5
Higher education instructors often lack timely and pedagogically grounded support, as scalable instructional guidance remains limited and existing tools rely on generic chatbot adv…
Can Decision Trees Teach Large Language Models? Distilling Verbalized Knowledge for Molecular Property Prediction
Khiem Le, Sreejata Dey, Marcos Martínez Galindo +4
Molecular Property Prediction (MPP) is a fundamental problem in drug discovery that has recently attracted growing attention. Large Language Models (LLMs), known for their impressi…
A Human-Centred AI System for Multi-Actor Planning and Collaboration in Family Learning
Si Chen, Jingyi Xie, Yao Li +8
Family learning takes place in everyday routines where children and caregivers read, practice, and develop new skills together. Despite growing interest in AI tutors, most existing…
Automated Benchmark Generation from Domain Guidelines Informed by Bloom's Taxonomy
Si Chen, Le Huy Khiem, Annalisa Szymanski +3
Open-ended question answering (QA) evaluates a model's ability to perform contextualized reasoning beyond factual recall. This challenge is especially acute in practice-based domai…
Transformation-Augmented GRPO for Enhancing Exploration in Reasoning of Large Language Models
Khiem Le, Phuc Nguyen, Youssef Mroueh +4
Group Relative Policy Optimization (GRPO) has become the dominant method for reinforcement learning with verifiable rewards in large language models, but it suffers from two critic…
CrochetBench: Can Vision-Language Models Move from Describing to Doing in Crochet Domain?
Peiyu Li, Xiaobao Huang, Ting Hua +1
While multimodal large language models can describe visual content, their ability to generate executable procedures remains underexplored. CrochetBench presented in this paper eval…
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…
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…
LLMs4All: A Review of Large Language Models Across Academic Disciplines
Yanfang Ye, Zheyuan Zhang, Tianyi Ma +26
Cutting-edge Artificial Intelligence (AI) techniques keep reshaping our view of the world. For example, Large Language Models (LLMs) based applications such as ChatGPT have shown t…
"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…
AgentDrug: Utilizing Large Language Models in An Agentic Workflow for Zero-Shot Molecular Editing
Khiem Le, Ting Hua, Nitesh V. Chawla
Molecular editing-modifying a given molecule to improve desired properties-is a fundamental task in drug discovery. While LLMs hold the potential to solve this task using natural l…
MolX: Enhancing Large Language Models for Molecular Understanding With A Multi-Modal Extension
Khiem Le, Zhichun Guo, Kaiwen Dong +8
Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields, moving beyond natural language understandi…