Interpreting and Editing Vision-Language Representations to Mitigate
  Hallucinations
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.
67002cd921c9ec08071448ab
Nick Jiang
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2025-06-09T10:14:54.474Z
6398d9d168e3392256aaf952
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Nick
user
nickjiang
67002cd921c9ec08071448ac
Anish Kachinthaya
67002cd921c9ec08071448ad
Suzie Petryk
67002cd921c9ec08071448ae
Yossi Gandelsman
Interpreting and Editing Vision-Language Representations to Mitigate
  Hallucinations
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.
Knowledge erasure enhances vision-language models' reliability by targeting hallucinations in latent representations, leading to improved performance and enabling zero-shot segmentation.
vision-language models internal representations hallucinations internal image representations language vocabulary output probabilities spatial localization knowledge erasure linear orthogonalization image features hallucinated object features latent representations zero-shot segmentation