9 papers · 1 filter
Encoder-Free Knowledge-Graph Reasoning with LLMs via Hyperdimensional Path Retrieval
Yezi Liu, William Youngwoo Chung, Hanning Chen +2
Recent progress in large language models (LLMs) has made knowledge-grounded reasoning increasingly practical, yet KG-based QA systems often pay a steep price in efficiency and tran…
Cauchy-Schwarz Fairness Regularizer
Yezi Liu, Hanning Chen, Wenjun Huang +2
Group fairness in machine learning is often enforced by adding a regularizer that reduces the dependence between model predictions and sensitive attributes. However, existing regul…
Mitigating Bias in Graph Hyperdimensional Computing
Yezi Liu, William Youngwoo Chung, Yang Ni +2
Graph hyperdimensional computing (HDC) has emerged as a promising paradigm for cognitive tasks, emulating brain-like computation with high-dimensional vectors known as hypervectors…
LUNE: Efficient LLM Unlearning via LoRA Fine-Tuning with Negative Examples
Yezi Liu, Hanning Chen, Wenjun Huang +2
Large language models (LLMs) possess vast knowledge acquired from extensive training corpora, but they often cannot remove specific pieces of information when needed, which makes i…
Recover-to-Forget: Gradient Reconstruction from LoRA for Efficient LLM Unlearning
Yezi Liu, Hanning Chen, Wenjun Huang +2
Unlearning in large foundation models (e.g., LLMs) is essential for enabling dynamic knowledge updates, enforcing data deletion rights, and correcting model behavior. However, exis…
MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation
Sanggeon Yun, Ryozo Masukawa, Minhyoung Na +1
In the context of escalating safety concerns across various domains, the tasks of Video Anomaly Detection (VAD) and Video Anomaly Recognition (VAR) have emerged as critically impor…