TourMart quantifies commission steering in LLM travel agents via paired counterfactual prompts, reporting 3.5-7.7 percentage point increases in steered recommendations for tested models.
arXiv preprint arXiv:2405.05905 , year=
7 Pith papers cite this work. Polarity classification is still indexing.
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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.
A novel online weighted aggregation mechanism for truthful preference feedback in mobile crowdsourcing achieves sublinear regret O(sqrt(T)) and truthfulness in a dynamic Bayesian game, with an extension for limited feedback per slot.
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.
Entity recognition models detect ads in RAG responses effectively and stay robust when advertisers switch styles, while lightweight models like random forests and SVMs become brittle under the same changes.
Generative AI advertising is reframed as a problem of trustworthy commercial intervention on the generative process, with a taxonomy of influence tiers from product mentions to long-term preference shaping.
LERA is a retrieve-then-generate auction system that refines ad candidate ranking with LLM logits and applies a threshold-aware critical-value payment rule to maintain truthfulness in chatbot ad insertion.
citing papers explorer
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TourMart: A Parametric Audit Instrument for Commission Steering in LLM Travel Agents
TourMart quantifies commission steering in LLM travel agents via paired counterfactual prompts, reporting 3.5-7.7 percentage point increases in steered recommendations for tested models.
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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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Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing
A novel online weighted aggregation mechanism for truthful preference feedback in mobile crowdsourcing achieves sublinear regret O(sqrt(T)) and truthfulness in a dynamic Bayesian game, with an extension for limited feedback per slot.
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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.
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Detecting RAG Advertisements Across Advertising Styles
Entity recognition models detect ads in RAG responses effectively and stay robust when advertisers switch styles, while lightweight models like random forests and SVMs become brittle under the same changes.
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Generative AI Advertising as a Problem of Trustworthy Commercial Intervention
Generative AI advertising is reframed as a problem of trustworthy commercial intervention on the generative process, with a taxonomy of influence tiers from product mentions to long-term preference shaping.
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LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots
LERA is a retrieve-then-generate auction system that refines ad candidate ranking with LLM logits and applies a threshold-aware critical-value payment rule to maintain truthfulness in chatbot ad insertion.