Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for improving the quality of generated contents of Large Language Models (LLMs) by grounding responses in external knowledge, thus reducing hallucinations and factual errors. However, recent studies have highlighted a critical vulnerability: advers...
Xingyu Lyu, Jia-Yi Wang, Jian-Feng He et al.· 0 citations
This work incorporates an additive structure into the DG framework and employs ℓq,1 -norm regularization to induce sparsity, thereby enabling structured feature selection and enhancing interpretability and presents two distinct realizations: an additive kernel-based formulation and a neural additive model-based approac...
Jia-Yi Wang, Han Li· Proceedings of the 32nd ACM...· 0 citations
Machine learning models continue to face challenges in out-of-distribution (OOD) generalization, where domain generalization (DG) aims to improve performance on unseen domains under distributional shifts. A prevalent paradigm in DG focuses on learning domain-invariant feature representations. However, feature represent...
Jiayi Wang, Han Li· Proceedings of the 32nd ACM...· 0 citations
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