We investigate auction mechanisms to support the emerging format of AI-generated content. We in particular study how to aggregate several LLMs in an incentive compatible manner. In this problem, the preferences of each agent over stochastically generated contents are described/encoded as an LLM. A key motivation is to design an auction format for AI-generated ad creatives to combine inputs from different advertisers. We argue that this problem, while generally falling under the umbrella of mechanism design, has several unique features. We propose a general formalism -- the token auction model -- for studying this problem. A key feature of this model is that it acts on a token-by-token basis and lets LLM agents influence generated contents through single dimensional bids. We first explore a robust auction design approach, in which all we assume is that agent preferences entail partial orders over outcome distributions. We formulate two natural incentive properties, and show that these are equivalent to a monotonicity condition on distribution aggregation. We also show that for such aggregation functions, it is possible to design a second-price auction, despite the absence of bidder valuation functions. We then move to designing concrete aggregation functions by focusing on specific valuation forms based on KL-divergence, a commonly used loss function in LLM. The welfare-maximizing aggregation rules turn out to be the weighted (log-space) convex combination of the target distributions from all participants. We conclude with experimental results in support of the token auction formulation.

翻译：我们研究拍卖机制以支持人工智能生成内容这一新兴形式。特别地，我们探讨如何以激励兼容的方式聚合多个大语言模型。在该问题中，各智能体对随机生成内容的偏好被描述/编码为大语言模型。核心动机是为人工智能生成的广告创意设计一种拍卖机制，以整合不同广告主的输入。我们认为该问题虽整体属于机制设计范畴，但具有若干独特特征。为此，我们提出一个通用形式化框架——令牌拍卖模型——用于研究该问题。该模型的关键特征在于逐令牌运作，并通过单维出价使大语言模型智能体影响生成内容。我们首先探索鲁棒拍卖设计方法，仅假设智能体偏好对结果分布构成偏序关系。我们定义两种自然的激励性质，并证明其等价于分布聚合的单调性条件。同时证明，尽管不存在竞标者估值函数，此类聚合函数仍可设计为二阶价格拍卖。随后，我们基于大语言模型中常用的损失函数KL散度，聚焦特定估值形式设计具体聚合函数。社会福利最大化聚合规则被证明是参与者目标分布的加权（对数空间）凸组合。最后，我们通过实验验证令牌拍卖模型的有效性。