Interpreting and Editing Vision-Language Representations to Mitigate
  Hallucinations
Authors: name: Nick Jiang, name: Anish Kachinthaya, name: Suzie Petryk, name: Yossi Gandelsman

We investigate the internal representations of vision-language models (VLMs)
to address hallucinations, a persistent challenge despite advances in model
size and training. We project VLMs' internal image representations to their
language vocabulary and observe more confident output probabilities on real
objects than hallucinated objects. We additionally use these output
probabilities to spatially localize real objects. Building on this approach, we
introduce a knowledge erasure algorithm that removes hallucinations by linearly
orthogonalizing image features with respect to hallucinated object features. We
show that targeted edits to a model's latent representations can reduce
hallucinations by up to 25.7% on the COCO2014 dataset while preserving
performance. Our findings demonstrate how a deeper understanding of VLMs'
latent representations can enhance reliability and enable novel capabilities,
such as zero-shot segmentation.