Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations (2410.02762v2)

Abstract: We investigate the internal representations of vision-LLMs (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.

• The paper demonstrates interpreting VLM internal representations to find differences between real and hallucinated objects, enabling targeted editing approaches.
• It introduces ProjectAway, a novel algorithm achieving up to 25.7% hallucination reduction on COCO2014 by selectively editing representations without impacting standard tasks like image captioning.
• The same representation analysis enables strong zero-shot object localization within images, outperforming baseline methods and providing an additional valuable capability.

An Examination of Vision-Language Representation Alterations for Hallucination Mitigation

The paper "Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations," authored by Nick Jiang and colleagues, provides a comprehensive analysis of internal representations in Vision-LLMs (VLMs) with the aim of addressing hallucinations—a persistent issue that remains unresolved despite the scaling of models. This paper focuses on exploiting the VLMs' internal features to identify and selectively erase hallucinations without detrimentally impacting model performance on standard tasks.

Core Methodology and Innovations

The paper pioneers an approach to understanding and manipulating the latent representations of VLMs explicitly directed at reducing the incidence of hallucinations. The methodology involves the projection of VLMs' internal image representations onto a text vocabulary and observing that real objects garner higher output probabilities than those imagined by the model. Taking advantage of this finding, the authors develop a spatial localization technique for identifying objects based on these output probability distributions.

The paper introduces a novel knowledge erasure algorithm named "ProjectAway," which performs a targeted linear orthogonalization against hallucinated object features within the image representations. This technique is employed to excise hallucinations effectively, achieving a reduction rate of up to 25.7% on the COCO2014 dataset without impairing operational model tasks like image captioning.

Significant Findings

1. Interpretative Proficiency: The paper demonstrates that through a logit lens approach, the VLM image representations exhibit discernible differences in confidence scores between real and hallucinated objects. This interpretative step is essential for subsequent object localization and knowledge editing tasks.
2. Object Localization Capability: The authors showcase that the same probability distributions used to identify hallucinations can also spatially localize objects within an image, thus contributing to improved zero-shot segmentation capabilities that compare favorably to state-of-the-art methods.
3. Reduction of Hallucinations: Utilizing ProjectAway, the approach achieves significant hallucination reduction without sacrificing performance in the captioning ability of the model. The method shows an increased effectiveness at erasing hallucinated objects as opposed to correctly detected ones, thus retaining model reliability and efficacy.
4. Zero-shot Segmentation: The proposed technique provides a direct application in zero-shot segmentation, where internal confidence values derived from image representations facilitate the spatial mapping of objects within an image, exceeding baseline methods without additional training requirements.

Implications for Future Research and Applications

The findings from this research have both practical and theoretical implications. Practically, the ability to identify and mitigate hallucinations in real-time enhances VLM applicability in sensitive domains where accuracy cannot be compromised, such as medical imaging and autonomous navigation. Theoretically, this demonstrates that internal representations in VLMs are not as intractable as previously considered; rather, they present a viable avenue for intervention to augment model reliability.

Future research could explore refinements to the ProjectAway algorithm, potentially extending its utility to more abstract representations, or incorporating multi-token object handling to improve performance in complex scenarios. Moreover, expanding similar approaches to different multimodal architectures could reinforce the generalization of these methods and their comprehensive impact across AI systems.

In conclusion, the paper by Jiang et al. points to a new direction in VLM research by underpinning the relationship between internal representations and output viability, effectively tying together the interpretation, manipulation, and application of these machine learning constructs to meet the demand for more resilient AI systems.