While the success of large language models (LLMs) increases demand for machine-generated text, current pay-per-token pricing schemes create a misalignment of incentives known in economics as moral hazard: Text-generating agents have strong incentive to cut costs by preferring a cheaper model over the cutting-edge one, and this can be done "behind the scenes" since the agent performs inference internally. In this work, we approach this issue from an economic perspective, by proposing a pay-for-performance, contract-based framework for incentivizing quality. We study a principal-agent game where the agent generates text using costly inference, and the contract determines the principal's payment for the text according to an automated quality evaluation. Since standard contract theory is inapplicable when internal inference costs are unknown, we introduce cost-robust contracts. As our main theoretical contribution, we characterize optimal cost-robust contracts through a direct correspondence to optimal composite hypothesis tests from statistics, generalizing a result of Saig et al. (NeurIPS'23). We evaluate our framework empirically by deriving contracts for a range of objectives and LLM evaluation benchmarks, and find that cost-robust contracts sacrifice only a marginal increase in objective value compared to their cost-aware counterparts.

翻译：尽管大型语言模型（LLM）的成功增加了对机器生成文本的需求，但当前按令牌付费的定价方案造成了经济学中称为道德风险的利益错配：文本生成代理有强烈的动机通过选择更便宜的模型而非尖端模型来降低成本，且这一行为可在“幕后”完成，因为代理在内部执行推理。在本工作中，我们从经济学视角出发，提出一种基于绩效付费的契约框架来激励文本质量。我们研究了一个委托-代理博弈，其中代理使用成本高昂的推理过程生成文本，而契约则根据自动质量评估结果确定委托方对文本的支付金额。由于当内部推理成本未知时标准契约理论不再适用，我们引入了成本鲁棒契约。作为主要的理论贡献，我们通过将其与统计学中的最优复合假设检验直接对应，刻画了最优成本鲁棒契约的特征，这推广了Saig等人（NeurIPS'23）的一个结果。我们通过为一系列目标函数和LLM评估基准推导具体契约，对我们的框架进行了实证评估，发现与已知成本的契约相比，成本鲁棒契约仅在目标函数值上牺牲了边际增量。