ConfusionPrompt enables private black-box LLM inference via prompt decomposition and pseudo-prompt mixing, claiming better privacy-utility trade-off than perturbation methods and lower memory use than open-source local models.
Large language models in medicine
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TrustLLM defines eight trustworthiness principles, creates a six-dimension benchmark, and evaluates 16 LLMs showing proprietary models generally lead but some open-source ones are close while over-calibration can hurt utility.
A four-module, discipline-agnostic course on AI-assisted literature review produces large self-reported confidence gains in hallucination detection, responsible use, and AI attribution.
citing papers explorer
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ConfusionPrompt: Practical Private Inference for Online Large Language Models
ConfusionPrompt enables private black-box LLM inference via prompt decomposition and pseudo-prompt mixing, claiming better privacy-utility trade-off than perturbation methods and lower memory use than open-source local models.
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TrustLLM: Trustworthiness in Large Language Models
TrustLLM defines eight trustworthiness principles, creates a six-dimension benchmark, and evaluates 16 LLMs showing proprietary models generally lead but some open-source ones are close while over-calibration can hurt utility.
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A Discipline-Agnostic AI Literacy Course for Academic Research: Architecture, Pedagogy, and Implementation
A four-module, discipline-agnostic course on AI-assisted literature review produces large self-reported confidence gains in hallucination detection, responsible use, and AI attribution.