Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations

2024-10-03

arXiv.org

AI Summary

To address the pervasive hallucination problem in vision-language models (VLMs)—i.e., generation of spurious object descriptions inconsistent with image content—this paper proposes a fine-tuning-free latent-space intervention method. The approach comprises two key components: (1) linearly projecting image features into the language token embedding space to enable interpretable identification and spatial localization of hallucinated objects; and (2) designing a knowledge-erasure algorithm that orthogonally suppresses hallucination-correlated features in a targeted manner. This work is the first to unify interpretability analysis and controllable editing of implicit VLM representations, enabling zero-shot segmentation and precise hallucination suppression. Evaluated on COCO2014, the method reduces hallucination rate by 25.7% while preserving original vision-language understanding performance without degradation.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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 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.

Problem

• Addressing hallucinations in vision-language models through internal representation analysis.
• Developing a knowledge erasure algorithm to mitigate hallucinated object features.
• Enhancing model reliability and enabling zero-shot segmentation capabilities.

Innovation

• Projects image to language vocabulary
• Uses output probabilities for localization
• Introduces knowledge erasure algorithm linearly