title: Interpreting and Editing Vision-Language Representations to Mitigate
    Hallucinations
summary: 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.
paper_authors._id: 67002cd921c9ec08071448ab
paper_authors.name: Nick Jiang
paper_authors._id: 67002cd921c9ec08071448ac
paper_authors.name: Anish Kachinthaya
paper_authors._id: 67002cd921c9ec08071448ad
paper_authors.name: Suzie Petryk
paper_authors._id: 67002cd921c9ec08071448ae
paper_authors.name: Yossi Gandelsman
paper_title: Interpreting and Editing Vision-Language Representations to Mitigate
    Hallucinations
paper_summary: 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.
paper_ai_keywords: vision-language models (VLMs) hallucinations internal image representations language vocabulary output probabilities spatial localization knowledge erasure algorithm image features hallucinated object features latent representations targeted edits zero-shot segmentation