ZIPP conditions diffusion models on LLM-rewritten prompts derived from graph-mined natural-language personas to achieve zero-shot personalization, reporting 13-20% gains and 79% human preference win rate over generic outputs.
In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
A self-supervised GNN model on cloud logs flags suspicious events with far fewer alerts than rule-based baselines but cannot estimate missed threats.
A graph neural network learns to simulate 1D sea ice floe collisions and trajectories using data assimilation on synthetic data.
citing papers explorer
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ZIPP:Zero-shot Image Personalization from Personas
ZIPP conditions diffusion models on LLM-rewritten prompts derived from graph-mined natural-language personas to achieve zero-shot personalization, reporting 13-20% gains and 79% human preference win rate over generic outputs.
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Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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Towards Improved Anomaly Detection for Cloud Cybersecurity via Graph Neural Networks
A self-supervised GNN model on cloud logs flags suspicious events with far fewer alerts than rule-based baselines but cannot estimate missed threats.
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Graph neural network for colliding particles with an application to sea ice floe modeling
A graph neural network learns to simulate 1D sea ice floe collisions and trajectories using data assimilation on synthetic data.