Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations

ICLR 2025 Conference Submission823 Authors

Keywords: Vision language models, hallucinations, logit lens, interpretability

Abstract: 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 identify differences in token output probabilities between real and 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.

Primary Area: interpretability and explainable AI

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Submission Number: 823