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.
arXiv preprint arXiv:2305.11482 , year=
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An extended annotation scheme with new categories and attributes plus a Gemma-300M-based multi-head classifier achieves 81.6% macro F1 on personal fact classification, outperforming few-shot LLM baselines by nearly 9 points with lower compute.
PUMA applies the Free Energy Principle to maintain beliefs over latent user states and select actions by minimizing expected free energy in multi-turn personalized dialogues.
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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An Annotation Scheme and Classifier for Personal Facts in Dialogue
An extended annotation scheme with new categories and attributes plus a Gemma-300M-based multi-head classifier achieves 81.6% macro F1 on personal fact classification, outperforming few-shot LLM baselines by nearly 9 points with lower compute.
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Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation
PUMA applies the Free Energy Principle to maintain beliefs over latent user states and select actions by minimizing expected free energy in multi-turn personalized dialogues.