<ul><li>A new research paper seeks to understand the evolution of knowledge in Large Vision-Language Models (LVLMs).</li><li>The study delves into the analysis of internal knowledge at different levels, including single token probabilities, token probability distributions, and feature encodings.</li><li>The research identifies two key nodes in knowledge evolution, namely critical layers and mutation layers, dividing the evolution process into rapid evolution, stabilization, and mutation.</li><li>This study provides valuable insights into the underlying mechanisms of LVLMs and contributes to their further enhancement.</li></ul>

Towards Understanding How Knowledge Evolves in Large Vision-Language Models

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