State-of-the-art large language models require specialized hardware and substantial energy to operate. As a consequence, cloud-based services that provide access to large language models have become very popular. In these services, the price users pay for an output provided by a model depends on the number of tokens the model uses to generate it: they pay a fixed price per token. In this work, we show that this pricing mechanism creates a financial incentive for providers to strategize and misreport the (number of) tokens a model used to generate an output, and users cannot prove, or even know, whether a provider is overcharging them. However, we also show that, if an unfaithful provider is obliged to be transparent about the generative process used by the model, misreporting optimally without raising suspicion is hard. Nevertheless, as a proof-of-concept, we develop an efficient heuristic algorithm that allows providers to significantly overcharge users without raising suspicion. Crucially, we demonstrate that the cost of running the algorithm is lower than the additional revenue from overcharging users, highlighting the vulnerability of users under the current pay-per-token pricing mechanism. Further, we show that, to eliminate the financial incentive to strategize, a pricing mechanism must price tokens linearly on their character count. While this makes a provider's profit margin vary across tokens, we introduce a simple prescription under which the provider who adopts such an incentive-compatible pricing mechanism can maintain the average profit margin they had under the pay-per-token pricing mechanism. Along the way, to illustrate and complement our theoretical results, we conduct experiments with several large language models from the $\texttt{Llama}$, $\texttt{Gemma}$ and $\texttt{Ministral}$ families, and input prompts from the LMSYS Chatbot Arena platform.

翻译：当前最先进的大语言模型需要专用硬件和大量能源才能运行。因此，提供大语言模型访问权限的云服务变得非常流行。在这些服务中，用户为模型生成的输出所支付的价格取决于模型生成该输出所使用的令牌数量：即按每个令牌固定价格付费。本研究证明，这种定价机制为服务提供商创造了策略性操作和虚报模型生成输出所用令牌数量的财务动机，而用户无法证明甚至不知晓提供商是否对其超额收费。然而，我们也发现，如果不可信的提供商被强制要求透明化模型使用的生成过程，则在不引起怀疑的情况下进行最优虚报将变得困难。尽管如此，作为概念验证，我们开发了一种高效启发式算法，使提供商能够在不可起用户怀疑的情况下显著超额收费。关键在于，我们证明该算法的运行成本低于通过超额收费获得的额外收入，凸显了当前按令牌付费定价机制下用户的脆弱性。此外，我们表明，要消除策略性操作的财务动机，定价机制必须按字符数对令牌进行线性定价。虽然这会导致提供商的利润率在不同令牌间存在差异，但我们引入了一种简单方案，使得采用这种激励兼容定价机制的提供商能够维持其原本在按令牌付费定价机制下的平均利润率。为说明和补充我们的理论结果，我们使用来自$\texttt{Llama}$、$\texttt{Gemma}$和$\texttt{Ministral}$系列的多个大语言模型，以及LMSYS Chatbot Arena平台的输入提示进行了实验。