Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes
We propose prototypical fine-tuning, a novel framework for fine-tuning pretrained language models that maintains robust performance across varying data sizes.
Jan 1, 2023
Predicting Information Pathways Across Online Communities
We develop methods to predict how information spreads across different online communities, revealing patterns in cross-platform information diffusion....
Jan 1, 2023
Reinforcement Subgraph Reasoning for Fake News Detection
A novel subgraph reasoning paradigm for fake news detection that provides explainability while improving generalization through reinforcement learning and hierarchical graph attention networks.
Jun 1, 2022
Towards Fine-Grained Reasoning for Fake News Detection
We propose a fine-grained reasoning framework for fake news detection by following the human information-processing model and designing a prior-aware bi-channel kernel graph network....
Jan 1, 2022