  Human beings are social creatures. We routinely reason about other agents,
and a crucial component of this social reasoning is inferring people's goals as
we learn about their actions. In many settings, we can perform intuitive but
reliable goal inference from language descriptions of agents, actions, and the
background environments. In this paper, we study this process of language
driving and influencing social reasoning in a probabilistic goal inference
domain. We propose a neuro-symbolic model that carries out goal inference from
linguistic inputs of agent scenarios. The "neuro" part is a large language
model (LLM) that translates language descriptions to code representations, and
the "symbolic" part is a Bayesian inverse planning engine. To test our model,
we design and run a human experiment on a linguistic goal inference task. Our
model closely matches human response patterns and better predicts human
judgements than using an LLM alone.
