Large Multi-modal Models Can Interpret Features in Large Multi-modal
  Models
Authors: name: Kaichen Zhang, name: Yifei Shen, name: Bo Li, name: Ziwei Liu

Recent advances in Large Multimodal Models (LMMs) lead to significant
breakthroughs in both academia and industry. One question that arises is how
we, as humans, can understand their internal neural representations. This paper
takes an initial step towards addressing this question by presenting a
versatile framework to identify and interpret the semantics within LMMs.
Specifically, 1) we first apply a Sparse Autoencoder(SAE) to disentangle the
representations into human understandable features. 2) We then present an
automatic interpretation framework to interpreted the open-semantic features
learned in SAE by the LMMs themselves. We employ this framework to analyze the
LLaVA-NeXT-8B model using the LLaVA-OV-72B model, demonstrating that these
features can effectively steer the model's behavior. Our results contribute to
a deeper understanding of why LMMs excel in specific tasks, including EQ tests,
and illuminate the nature of their mistakes along with potential strategies for
their rectification. These findings offer new insights into the internal
mechanisms of LMMs and suggest parallels with the cognitive processes of the
human brain.