70 citations · 83 across the 31 of their papers we have counts for
13 papers · 1 filter
Why AI Detection Fails for Academic Integrity
Jonathan A. Karr, Grigorii Khvatskii, Ting Hua +1
Institutions use commercial AI detectors for academic integrity, yet detectors cannot distinguish AI editing from full LLM drafts and may treat both as misconduct. In a controlled…
Do LLMs have core beliefs?
Anna Sokol, Marianna B. Ganapini, Nitesh V. Chawla
The rise of Large Language Models (LLMs) has sparked debate about whether these systems exhibit human-level cognition. In this debate, little attention has been paid to a structura…
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…
Policy4OOD: A Knowledge-Guided World Model for Policy Intervention Simulation against the Opioid Overdose Crisis
Yijun Ma, Zehong Wang, Weixiang Sun +4
The opioid epidemic remains one of the most severe public health crises in the United States, yet evaluating policy interventions before implementation is difficult: multiple polic…
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…
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…
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…
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…
Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
Zehong Wang, Zheyuan Zhang, Tianyi Ma +3
Foundation models are pretrained on large-scale corpora to learn generalizable patterns across domains and tasks -- such as contours, textures, and edges in images, or tokens and s…
GFT: Graph Foundation Model with Transferable Tree Vocabulary
Zehong Wang, Zheyuan Zhang, Nitesh V Chawla +2
Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Gr…
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…
A Survey of Large Language Models for Graphs
Xubin Ren, Jiabin Tang, Dawei Yin +2
Graphs are an essential data structure utilized to represent relationships in real-world scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver impress…