Consider a marketplace of AI tools, each with slightly different strengths and weaknesses. By selecting the right model for the task at hand, a user can do better than simply committing to a single model for everything. Routers operate under a similar principle, where sophisticated model selection can increase overall performance. However, aggregation is often noisy, reflecting in imperfect user choices or routing decisions. This leads to two main questions: first, what does a "healthy marketplace" of models look like for maximizing consumer utility? Secondly, how can we incentivize producers to create such models? Here, we study two types of model changes: market entry (where an entirely new model is created and added to the set of available models), and model replacement (where an existing model has its strengths and weaknesses changed). We show that winrate, a standard benchmark in LLM evaluation, can incentivize model creators to homogenize for both types of model changes, reducing consumer welfare. We propose a new mechanism, weighted winrate, which rewards models for answers that are higher quality, and show that it provably improves incentives for producers to specialize and increases consumer welfare. We conclude by demonstrating that our theoretical results generalize to empirical benchmark datasets and discussing implications for evaluation design.

翻译：考虑一个由多种AI工具构成的市场，每个工具在优势与劣势方面存在细微差异。通过为当前任务选择合适的模型，用户能够获得比始终使用单一模型更优的性能。路由机制遵循类似原则，其中精细化的模型选择可以提升整体表现。然而，聚合过程往往存在噪声，这反映在用户的不完美选择或路由决策中。由此引出两个核心问题：首先，为最大化用户效用，怎样的模型市场可被视为"健康市场"？其次，我们如何激励生产者创建此类模型？本文研究两种模型变更类型：市场进入（即创建全新模型并加入可用模型集合）与模型替换（即改变现有模型的优劣势）。我们证明，作为大语言模型评估标准基准的胜率指标，可能激励模型创建者在两种变更类型中趋向同质化，从而降低用户福利。我们提出一种新机制——加权胜率，该机制通过奖励提供更高质量答案的模型，可证明地改善生产者专业化的激励并提升用户福利。最后，我们通过实证基准数据集验证理论结果的普适性，并探讨其对评估设计的影响。