1 citations · 1 across the 4 of their papers we have counts for
4 papers
Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling
Yiran Guo, Zhongjian Qiao, Yingqi Xie +5
Effective exploration is a key challenge in reinforcement learning for large language models: discovering high-quality trajectories within a limited sampling budget from the vast n…
Inferring Latent Market Forces: Evaluating LLM Detection of Gamma Exposure Patterns via Obfuscation Testing
Christopher Regan, Ying Xie
We introduce obfuscation testing, a novel methodology for validating whether large language models detect structural market patterns through causal reasoning rather than temporal a…
KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Guanzhi Deng, Yi Xie, Yu-Keung Ng +6
Effectively managing intellectual property is a significant challenge. Traditional methods for patent analysis depend on labor-intensive manual searches and rigid keyword matching.…
Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learning
Lili Zhang, Quanyan Zhu, Herman Ray +1
Network threat detection has been challenging due to the complexities of attack activities and the limitation of historical threat data to learn from. To help enhance the existing…