Impacts of Aggregation on Model Diversity and Consumer Utility

TL;DR

This paper examines how aggregation impacts model diversity and consumer utility in AI marketplaces, proposing a new evaluation mechanism to incentivize better model specialization and improve overall consumer benefits.

Contribution

It introduces weighted winrate as a new incentive mechanism that promotes model diversity and consumer welfare, addressing limitations of standard winrate evaluations.

Findings

Weighted winrate incentivizes model specialization.

Homogenization occurs under standard winrate, reducing consumer utility.

Theoretical results extend to empirical benchmark datasets.

Abstract

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.