Large language models as human proxies

The idea of computationally approximating human behavior is being reshaped by the emergence of large language models (LLMs). Although LLMs do not explain the mechanisms of human behavior, they can potentially stand in for humans-giving rise to new applied and scientific uses. Here we analyze research from multiple disciplines that explicitly or implicitly use LLMs as human proxies and organize them into four roles: believable agents, task agents, experimental subjects and silicon samples. By comparing the roles, we show that similarity to humans is not a single feature: each supports a different scientific claim and requires its own validity criteria.