Text or Pixels? It Takes Half: On the Token Efficiency of Visual Text
  Inputs in Multimodal LLMs
Authors: name: Yanhong Li, name: Zixuan Lan, name: Jiawei Zhou

Large language models (LLMs) and their multimodal variants can now process
visual inputs, including images of text. This raises an intriguing question:
can we compress textual inputs by feeding them as images to reduce token usage
while preserving performance? In this paper, we show that visual text
representations are a practical and surprisingly effective form of input
compression for decoder LLMs. We exploit the idea of rendering long text inputs
as a single image and provide it directly to the model. This leads to
dramatically reduced number of decoder tokens required, offering a new form of
input compression. Through experiments on two distinct benchmarks RULER
(long-context retrieval) and CNN/DailyMail (document summarization) we
demonstrate that this text-as-image method yields substantial token savings
(often nearly half) without degrading task performance.