2 citations · 2 across the 12 of their papers we have counts for
12 papers
Do Large Language Models Know What They Don't Know II? A Fully Behavioral, Non-Cognitive Measure of Epistemic Honesty
Ali Şenol, H. Russell Bernard, Huan Liu
Large Language Models (LLMs) are frequently confident, eloquent, and well versed. A natural question arises: do they know what they don't know? To answer this question, we borrow t…
MUGEN: Generating Unlearnable Graph Examples for Multiple Learning Tasks
Ziyan Liu, Chengshuai Zhao, Huan Liu
Graph data across diverse domains can expose valuable relational information to unauthorized representation learning, creating a pressing need for protection against such misuse. U…
To See is Not to Learn: Protecting Multimodal Data from Unauthorized Fine-Tuning of Large Vision-Language Model
Chengshuai Zhao, Zhen Tan, Dawei Li +2
The rapid advancement of Large Vision-Language Models (LVLMs) is increasingly accompanied by unauthorized scraping and training on multimodal web data, posing severe copyright and…
Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence
Ujun Jeong, Saketh Vishnubhatla, Bohan Jiang +3
During disasters, extracting causal relations from social media can strengthen situational awareness by identifying factors linked to casualties, physical damage, infrastructure di…
The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction
Shu Wan, Abhinav Gorantla, Huan Liu +1
Under standard graphical assumptions, the Markov boundary of a target variable is the smallest set of features that renders every other feature redundant. Once the boundary is obse…
Measuring Reasoning Quality in LLMs: A Multi-Dimensional Behavioral Framework
Ali Şenol, Garima Agrawal, Huan Liu
Despite remarkable progress on reasoning benchmarks, current LLM evaluation practice remains anchored to final-answer correctness, providing limited insight into how models reason,…
AvAtar: Learning to Align via Active Optimal Transport
Qi Yu, Ruizhong Qiu, Zhichen Zeng +3
Alignment plays a fundamental role in many machine learning problems, such as multi-network analysis, multimodal learning, and point cloud registration. Recent works increasingly l…
REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models for Trustworthy Open-Ended Grading
Chengshuai Zhao, Fan Zhang, Kumar Satvik Chaudhary +4
Open-ended grading is central to equitable and personalized education, yet manual grading remains time-consuming and costly, underscoring the need for automated grading systems. Al…
GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems
Hongjiang Chen, Xin Zheng, Yixin Liu +7
Large language model (LLM)-based multi-agent systems (MAS) have demonstrated exceptional capabilities in solving complex tasks, yet their effectiveness depends heavily on the under…
Probing to Refine: Reinforcement Distillation of LLMs via Explanatory Inversion
Zhen Tan, Chengshuai Zhao, Song Wang +3
Distilling robust reasoning capabilities from large language models (LLMs) into smaller, computationally efficient student models remains an unresolved challenge. Despite recent ad…
Label Distribution Learning-Enhanced Dual-KNN for Text Classification
Bo Yuan, Yulin Chen, Zhen Tan +3
Many text classification methods usually introduce external information (e.g., label descriptions and knowledge bases) to improve the classification performance. Compared to extern…
Ontology-Aware RAG for Improved Question-Answering in Cybersecurity Education
Chengshuai Zhao, Garima Agrawal, Fan Zhang +5
Integrating AI into education has the potential to transform the teaching of science and technology courses, particularly in the field of cybersecurity. AI-driven question-answerin…