When organizations delegate text generation tasks to AI providers via pay-for-performance contracts, expected payments rise when evaluation is noisy. As evaluation methods become more elaborate, the economic benefits of decreased noise are often overshadowed by increased evaluation costs. In this work, we introduce adaptive contracts for AI delegation, which allow detailed evaluation to be performed selectively after observing an initial coarse signal in order to conserve resources. We make three sets of contributions: First, we provide efficient algorithms for computing optimal adaptive contracts under natural assumptions or when core problem dimensions are small, and prove hardness of approximation in the general unstructured case. We then formulate alternative models of randomized adaptive contracts and discuss their benefits and limitations. Finally, we empirically demonstrate the benefits of adaptivity over non-adaptive baselines using question-answering and code-generation datasets.

翻译：当组织通过按绩效付费合约将文本生成任务委托给AI提供商时，若评估存在噪声，预期支付将随之增加。随着评估方法日趋复杂，噪声降低带来的经济效益往往被评估成本的上升所抵消。本研究提出一种用于AI委托的自适应合约机制，该机制允许在观测到初始粗略信号后，有选择性地执行详细评估，从而节约资源。我们做出三方面贡献：首先，在自然假设或核心问题维度较小时，我们提供了计算最优自适应合约的高效算法，并证明了一般非结构化情形下的近似求解困难性。其次，我们构建了随机化自适应合约的替代模型，并讨论其优势与局限。最后，我们基于问答与代码生成数据集进行实证研究，验证了自适应机制相较于非自适应基线的优越性。