  Human models play a crucial role in human-robot interaction (HRI), enabling
robots to consider the impact of their actions on people and plan their
behavior accordingly. However, crafting good human models is challenging;
capturing context-dependent human behavior requires significant prior knowledge
and/or large amounts of interaction data, both of which are difficult to
obtain. In this work, we explore the potential of large-language models (LLMs)
-- which have consumed vast amounts of human-generated text data -- to act as
zero-shot human models for HRI. Our experiments on three social datasets yield
promising results; the LLMs are able to achieve performance comparable to
purpose-built models. That said, we also discuss current limitations, such as
sensitivity to prompts and spatial/numerical reasoning mishaps. Based on our
findings, we demonstrate how LLM-based human models can be integrated into a
social robot's planning process and applied in HRI scenarios. Specifically, we
present one case study on a simulated trust-based table-clearing task and
replicate past results that relied on custom models. Next, we conduct a new
robot utensil-passing experiment (n = 65) where preliminary results show that
planning with a LLM-based human model can achieve gains over a basic myopic
plan. In summary, our results show that LLMs offer a promising (but incomplete)
approach to human modeling for HRI.
