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This survey presents a unified and pipeline-aware overview of RAG robustness, formalize threat models over the corpus, retriever, and generator, and organize attacks into three main objectives: accuracy, privacy, and fairness.
A five-stage semi-automatic framework for constructing complex graph reasoning benchmarks that serves as a challenging and diagnostic benchmark for graph reasoning and provides empirical guidance for future enhancement methods is proposed.
This work presents the first systematic survey of graph-assisted LLMs from the perspective of how graph structures mitigate LLMs’ limitations, and introduces a taxonomy spanning Graph-Assisted Knowledge Augmentation, Graph-Assisted Reasoning and Planning, and Graph-Assisted LLM Collaboration.
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