  Co-speech gesturing is an important modality in conversation, providing
context and social cues. In character animation, appropriate and synchronised
gestures add realism, and can make interactive agents more engaging.
Historically, methods for automatically generating gestures were predominantly
audio-driven, exploiting the prosodic and speech-related content that is
encoded in the audio signal. In this paper we instead experiment with using LLM
features for gesture generation that are extracted from text using LLAMA2. We
compare against audio features, and explore combining the two modalities in
both objective tests and a user study. Surprisingly, our results show that
LLAMA2 features on their own perform significantly better than audio features
and that including both modalities yields no significant difference to using
LLAMA2 features in isolation. We demonstrate that the LLAMA2 based model can
generate both beat and semantic gestures without any audio input, suggesting
LLMs can provide rich encodings that are well suited for gesture generation.
