7 papers
Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection
Xuwei Tan, Yao Ma, Xueru Zhang
Detecting fraud in financial transactions typically relies on tabular models that demand heavy feature engineering to handle high-dimensional data and offer limited interpretabilit…
Adversarial Déjà Vu: Jailbreak Dictionary Learning for Stronger Generalization to Unseen Attacks
Mahavir Dabas, Tran Huynh, Nikhil Reddy Billa +8
Large language models remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs. Defending against novel jailbreaks represents a critical chall…
SaVe-TAG: LLM-based Interpolation for Long-Tailed Text-Attributed Graphs
Leyao Wang, Yu Wang, Bo Ni +4
Real-world graph data often follows long-tailed distributions, making it difficult for Graph Neural Networks (GNNs) to generalize well across both head and tail classes. Recent adv…
Knowledge Homophily in Large Language Models
Utkarsh Sahu, Zhisheng Qi, Mahantesh Halappanavar +6
Large Language Models (LLMs) have been increasingly studied as neural knowledge bases for supporting knowledge-intensive applications such as question answering and fact checking.…
Modeling Insider Filing Delays in Financial Markets with an Interpretable XGBoost Framework
Cheng Huang, Yao Ma, Fan Gao +10
Timely disclosure of insider transactions is a cornerstone of market transparency, yet delays in filing remain widespread and challenging to monitor at scale. This study introduces…
CryptGNN: Enabling Secure Inference for Graph Neural Networks
Pritam Sen, Yao Ma, Cristian Borcea
We present CryptGNN, a secure and effective inference solution for third-party graph neural network (GNN) models in the cloud, which are accessed by clients as ML as a service (MLa…