Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference
arXiv:2606.17165v1 Announce Type: cross Abstract: Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at lower cost. We study when a treatment effect estimated on LLM outcomes recovers the effect that would have been measured on the human population of interest. Distributional equivalence between LLM and human outcomes would make any standard estimator valid but is unrealistic. We therefore develop a statistical framework that adapts surrogate endpoint theory to LLMs. T
The rapid advancement and adoption of large language models (LLMs) are driving immediate research into their practical applications and limitations in industrial settings like A/B testing.
This research provides a statistical framework to validate LLM-based A/B testing, which could significantly reduce the cost and speed of experimentation across various industries, impacting product development and marketing efficiency.
The ability to reliably substitute human participants with LLMs in A/B testing can fundamentally alter how product and feature development cycles operate, making them faster and cheaper.
- · AI-driven research platforms
- · Product development teams
- · Marketing agencies
- · Software companies
- · Traditional A/B testing service providers
- · Human participant recruitment agencies
Companies will increasingly rely on LLM-powered simulations for rapid product iteration and optimization.
This could lead to a 'simulation advantage' for firms with advanced LLM capabilities, accelerating their market responsiveness.
Ethical and regulatory discussions will intensify regarding the representativeness and potential biases of using LLMs as proxies for human populations.
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Read at arXiv cs.AI