Qiang Hu
3 indexed papers
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E-MIA introduces a novel, stealthy black-box membership inference attack that converts verifiable hard evidence within a candidate document into an objective, multi-part exam score to determine if the document was ingested into a RAG knowledge base.
The paper addresses the gap in understanding real-world LLM-in-the-loop vulnerabilities by creating the LLMCVE dataset and demonstrating that these vulnerabilities are significantly harder to repair than conventional software flaws.
The paper introduces LL-Bench, a comprehensive benchmark for evaluating large-scale generative models on low-level vision tasks, and proposes LL-Score, an MLLM-based evaluator that better aligns quality assessment with human preferences.
Papers
LL-Bench: Rethinking Low-Level Vision Evaluation in the Era of Large-Scale Generative Models
Lu Liu, Huiyu Duan, Chenxin Zhu, Jintong Lu +5 more
The paper introduces LL-Bench, a comprehensive benchmark for evaluating large-scale generative models on low-level vision tasks, and proposes LL-Score, an MLLM-based evaluator that better aligns quali…