6 papers
Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns
Sishun Liu, Sajal Halder, Ke Deng +2
Information Operations on social media networks have been identified as a significant threat to democracy and modern society, but they are challenging and expensive to detect by hu…
Online Skill Learning for Web Agents via State-Grounded Dynamic Retrieval
Jiaxi Li, Ke Deng, Yun Wang +5
Language agents increasingly rely on reusable skills to improve multi-step web automation across related tasks. A growing line of work studies online skill learning, where agents c…
Learning Marked Temporal Point Process Explanations based on Counterfactual and Factual Reasoning
Sishun Liu, Ke Deng, Xiuzhen Zhang +1
Neural network-based Marked Temporal Point Process (MTPP) models have been widely adopted to model event sequences in high-stakes applications, raising concerns about the trustwort…
Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques
Xiaofei Xu, Xiuzhen Zhang, Ke Deng
Fake news and misinformation poses a significant threat to society, making efficient mitigation essential. However, manual fact-checking is costly and lacks scalability. Large Lang…
Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning
Xiaofei Xu, Ke Deng, Michael Dann +1
This study aims to minimize the influence of fake news on social networks by deploying debunkers to propagate true news. This is framed as a reinforcement learning problem, where,…
Explainable History Distillation by Marked Temporal Point Process
Sishun Liu, Ke Deng, Yan Wang +1
Explainability of machine learning models is mandatory when researchers introduce these commonly believed black boxes to real-world tasks, especially high-stakes ones. In this pape…