5 papers
Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization
Yang Qiu, Yixiong Zou, Jun Wang +3
Out-of-distribution generalization under distributional shifts remains a critical challenge for graph neural networks. Existing methods generally adopt the Invariant Risk Minimizat…
Is Model Editing Built on Sand? Revealing Its Illusory Success and Fragile Foundation
Wei Liu, Haomei Xu, Bingqing Liu +6
Large language models (LLMs) inevitably encode outdated or incorrect knowledge. Updating, deleting, and forgetting such knowledge is important for alignment, safety, and other issu…
Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables
Xuzhao Geng, Haozhao Wang, Jun Wang +2
Retrieval-augmented generation (RAG) is a key technique for leveraging external knowledge and reducing hallucinations in large language models (LLMs). However, RAG still struggles…
Adversarial Attack for Explanation Robustness of Rationalization Models
Yuankai Zhang, Lingxiao Kong, Haozhao Wang +4
Rationalization models, which select a subset of input text as rationale-crucial for humans to understand and trust predictions-have recently emerged as a prominent research area i…
PAGE: Parametric Generative Explainer for Graph Neural Network
Yang Qiu, Wei Liu, Jun Wang +1
This article introduces PAGE, a parameterized generative interpretive framework. PAGE is capable of providing faithful explanations for any graph neural network without necessitati…