Controlling Object Hallucination in Large Multimodal Models

Core Concepts

Detailed image captioning in large multimodal models (LMMs) suffers from object existence hallucination, where the model references objects not present in the image. This work analyzes the underlying causes of such hallucination and introduces HallE-Control, a controllable LMM that can shift between exclusively depicting contextually grounded objects and blending them with parametrically inferred objects.

Abstract

The paper investigates the problem of object existence hallucination in detailed image captioning using large multimodal models (LMMs). It first outlines three types of hallucination: object existence, attribute, and relationship hallucination.
To better evaluate object existence hallucination, the authors introduce CCEval, a GPT-4 assisted evaluation method that maintains consistency in metrics like average sentence length and number of objects. CCEval reveals that even models that perform well on VQA-based benchmarks exhibit substantial hallucinations in detailed captions.
The paper then systematically analyzes the factors influencing hallucination, including the size of the language decoder, the quantity and quality of instruction data, and the input resolution to the vision encoder. The key finding is that the misalignment between objects mentioned in the training captions and those that the vision encoder can effectively ground is the primary cause of hallucination.
To address this issue, the authors present HallE-Control, a novel approach that controls the extent of expressed hallucination or parametric knowledge. HallE-Control is trained on a dataset that captures both pure contextual knowledge and a blend of contextual and parametric knowledge. During inference, a single continuous parameter adjustment enables the model to produce detailed captions with only contextually grounded objects or a blend of contextual and parametrically inferred objects. This method reduces hallucination by 44% compared to the baseline while maintaining object coverage.

Stats

The image displays a bustling street scene with an old-fashioned car and a white bus.
In the background, a series of trees and overhead cables are visible, suggesting an urban setting.

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Key Insights Distilled From

HallE-Control

by Bohan Zhai,S... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2310.01779.pdf

Deeper Inquiries

How can the proposed HallE-Control approach be extended to address other types of hallucination, such as attribute and relationship hallucination, in detailed image captioning?

In order to extend the HallE-Control approach to address attribute and relationship hallucination in detailed image captioning, we can adapt the control mechanism to focus on controlling the generation of attributes and relationships in addition to objects. This can be achieved by introducing specific markers or indicators in the training data to highlight attribute and relationship information that may be prone to hallucination.
For attribute hallucination, the model can be trained to recognize and differentiate between accurate attribute descriptions and potentially hallucinated ones. By incorporating a similar control parameter as in HallE-Control, the model can adjust the emphasis on generating attribute details based on the context and grounding in the image.
Similarly, for relationship hallucination, the model can be guided to distinguish between accurate depictions of object interactions and potentially erroneous relationships. The control parameter can be utilized to modulate the level of detail and accuracy in describing relationships between objects in the image.
By extending the HallE-Control approach to address attribute and relationship hallucination, the model can learn to balance the generation of detailed captions with a focus on accurate attributes and relationships, reducing the likelihood of hallucinations in these aspects of image captioning.

How can the potential implications of allowing parametric knowledge in the captions be addressed, and how can the model be further refined to strike a balance between accuracy and imagination?

Allowing parametric knowledge in the captions can lead to improved flexibility and creativity in generating detailed image captions. However, to address the potential implications of this, it is essential to refine the model to strike a balance between accuracy and imagination. One approach to achieve this balance is through fine-tuning the control mechanism in HallE-Control to regulate the extent of parametric knowledge utilization.
To refine the model, the control parameter can be optimized to dynamically adjust the level of reliance on parametric knowledge based on the context and grounding of objects in the image. By fine-tuning the control mechanism during training, the model can learn to prioritize contextual information while incorporating parametric knowledge in a controlled manner to enhance the richness of the generated captions without compromising accuracy.
Additionally, incorporating feedback mechanisms or reinforcement learning techniques can help the model learn to self-regulate the use of parametric knowledge based on the quality and relevance of the generated captions. By iteratively refining the control mechanism and optimizing the balance between accuracy and imagination, the model can achieve a more nuanced and reliable performance in detailed image captioning.

How might the insights from this work on object hallucination in image captioning be applied to other multimodal tasks, such as visual question answering or visual dialogue, to improve their reliability and robustness?

The insights gained from addressing object hallucination in image captioning can be applied to other multimodal tasks, such as visual question answering (VQA) or visual dialogue, to enhance their reliability and robustness.

Alignment between Vision and Language: Ensuring alignment between visual information and language descriptions is crucial in VQA and visual dialogue tasks. By addressing misalignments that lead to hallucinations in object descriptions, models can improve their understanding of visual content and generate more accurate responses.

Controlled Imagination: Implementing a control mechanism similar to HallE-Control can help regulate the generation of responses in VQA and visual dialogue tasks. By controlling the extent of imagination and reliance on parametric knowledge, models can provide more reliable and contextually grounded answers.

Fine-tuning for Task-specific Requirements: Adapting the model's training and fine-tuning process to specific task requirements in VQA and visual dialogue can help optimize performance. By incorporating insights on hallucination and control mechanisms, models can be tailored to different tasks and datasets for improved reliability and robustness.

By leveraging the learnings from object hallucination in image captioning and applying them to other multimodal tasks, models can enhance their interpretability, accuracy, and overall performance in understanding and generating responses based on visual and textual inputs.