NaiAD is a new dataset and framework for LLM-native advertising that uses decoupled generation and calibrated scoring to identify four semantic strategies for balancing user and commercial utilities.
Position auctions in ai-generated content
3 Pith papers cite this work. Polarity classification is still indexing.
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Neuron Auctions auction continuous neuron intervention budgets on brand-specific orthogonal subspaces in LLMs to achieve strategy-proof revenue optimization while penalizing user utility loss.
A quality-preserving auction framework for LLM advertising uses RAG-based endogenous reserves and KL-regularized or screened VCG mechanisms to achieve DSIC, IR, higher revenue, and better semantic fidelity than baselines.
citing papers explorer
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NaiAD: Initiate Data-Driven Research for LLM Advertising
NaiAD is a new dataset and framework for LLM-native advertising that uses decoupled generation and calibrated scoring to identify four semantic strategies for balancing user and commercial utilities.
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LLM Advertisement based on Neuron Auctions
Neuron Auctions auction continuous neuron intervention budgets on brand-specific orthogonal subspaces in LLMs to achieve strategy-proof revenue optimization while penalizing user utility loss.
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Mechanism Design for Quality-Preserving LLM Advertising
A quality-preserving auction framework for LLM advertising uses RAG-based endogenous reserves and KL-regularized or screened VCG mechanisms to achieve DSIC, IR, higher revenue, and better semantic fidelity than baselines.