Lost in Embeddings: Information Loss in Vision-Language Models
Authors: name: Wenyan Li, name: Raphael Tang, name: Chengzu Li, name: Caiqi Zhang, name: Ivan Vulić, name: Anders Søgaard

Vision--language models (VLMs) often process visual inputs through a
pretrained vision encoder, followed by a projection into the language model's
embedding space via a connector component. While crucial for modality fusion,
the potential information loss induced by this projection step and its direct
impact on model capabilities remain understudied. We introduce two
complementary approaches to examine and quantify this loss by analyzing the
latent representation space. First, we evaluate semantic information
preservation by analyzing changes in k-nearest neighbor relationships between
image representations, before and after projection. Second, we directly measure
information loss by reconstructing visual embeddings from the projected
representation, localizing loss at an image patch level. Experiments reveal
that connectors substantially distort the local geometry of visual
representations, with k-nearest neighbors diverging by 40--60\%
post-projection, correlating with degradation in retrieval performance. The
patch-level embedding reconstruction provides interpretable insights for model
behavior on visually grounded question-answering tasks, finding that areas of
high information loss reliably predict instances where models struggle.