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A commercial incentive need not enter the final ranking algorithm to affect a shopping assistant's recommendation: it may instead influence which preference question the assistant asks. We make this d...
arXiv:2604. 08525v2 Announce Type: replace Abstract: Large language models (LLMs) are trained to align with user preferences through methods like reinforcement learning.
The paper investigates how role assignment in large language model (LLM) recommenders influences sponsorship bias. By assigning the agent’s principal as either a traveler or a booking platform, the authors find that platform delegation reduces the penalty applied to sponsored listings and weakens consumer skepticism triggered by disclosure. The study also shows that stricter terminology and attribution to the platform widen the divergence in agent evaluations, indicating that current disclosure mandates are insufficient to protect consumers in AI-mediated commerce.
arXiv:2606. 17443v1 Announce Type: new Abstract: Large language models (LLMs) are becoming a major way for consumers to find products, but we do not yet understand how brands compete in this new channel.
arXiv:2606. 22974v2 Announce Type: replace Abstract: Recent work on preference elicitation in large language models (LLMs) has demonstrated that, when given a series of choices between two outcomes, LLMs reveal a coherent, model-specific utility structure.
arXiv:2601.19435v2 Announce Type: replace-cross Abstract: Sustainable monetization of large language models (LLMs) remains a critical open challenge. Traditional search advertising, which relies on s...