  Gestures perform a variety of communicative functions that powerfully
influence human face-to-face interaction. How this communicative function is
achieved varies greatly between individuals and depends on the role of the
speaker and the context of the interaction. Approaches to automatic gesture
generation vary not only in the degree to which they rely on data-driven
techniques but also the degree to which they can produce context and speaker
specific gestures. However, these approaches face two major challenges: The
first is obtaining sufficient training data that is appropriate for the context
and the goal of the application. The second is related to designer control to
realize their specific intent for the application. Here, we approach these
challenges by using large language models (LLMs) to show that these powerful
models of large amounts of data can be adapted for gesture analysis and
generation. Specifically, we used ChatGPT as a tool for suggesting
context-specific gestures that can realize designer intent based on minimal
prompts. We also find that ChatGPT can suggests novel yet appropriate gestures
not present in the minimal training data. The use of LLMs is a promising avenue
for gesture generation that reduce the need for laborious annotations and has
the potential to flexibly and quickly adapt to different designer intents.
