Cascaded Language Models for Cost-Effective Human–AI Decision-Making

A challenge in human-AI decision-making is to balance three factors: the of predictions, the of knowledge and reasoning complexity, and the confidence about whether to from automated answers or escalate to human experts. In this work, we present a cascaded LLM decision framework that adaptively delegates tasks across multiple tiers of expertise -- a base model for initial candidate answers, a more capable and knowledgeable (but costlier) large model, and a human expert for when the model cascade abstains.