3 papers
cs.LG2025
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…
cs.AI2025
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Large language models (LLMs) inevitably encode outdated or incorrect knowledge.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…
cs.CL2025
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…