  The rapid development of large language and vision models (LLVMs) has been
driven by advances in visual instruction tuning. Recently, open-source LLVMs
have curated high-quality visual instruction tuning datasets and utilized
additional vision encoders or multiple computer vision models in order to
narrow the performance gap with powerful closed-source LLVMs. These
advancements are attributed to multifaceted information required for diverse
capabilities, including fundamental image understanding, real-world knowledge
about common-sense and non-object concepts (e.g., charts, diagrams, symbols,
signs, and math problems), and step-by-step procedures for solving complex
questions. Drawing from the multifaceted information, we present a new
efficient LLVM, Mamba-based traversal of rationales (Meteor), which leverages
multifaceted rationale to enhance understanding and answering capabilities. To
embed lengthy rationales containing abundant information, we employ the Mamba
architecture, capable of processing sequential data with linear time
complexity. We introduce a new concept of traversal of rationale that
facilitates efficient embedding of rationale. Subsequently, the backbone
multimodal language model (MLM) is trained to generate answers with the aid of
rationale. Through these steps, Meteor achieves significant improvements in
vision language performances across multiple evaluation benchmarks requiring
diverse capabilities, without scaling up the model size or employing additional
vision encoders and computer vision models.
