Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

Core Concepts

Detecting and mitigating hallucinations in large vision language models (LVLMs) via fine-grained AI feedback from powerful closed-source models to improve the fidelity of LVLM responses.

Abstract

This paper proposes a method for detecting and mitigating hallucinations in large vision language models (LVLMs) using fine-grained AI feedback. The key components are:

Fine-Grained AI Feedback: The authors generate a small-size sentence-level hallucination annotation dataset using powerful closed-source models like GPT-4 and GPT-4V. This feedback covers the primary hallucination types (object, attribute, and relationship).

Fine-Grained Hallucination Detection: The authors train a hallucination detection model on the fine-grained AI feedback, which can perform sentence-level hallucination detection and provide severity scores.

Detect-then-Rewrite Pipeline for Preference Dataset: The authors propose an automatic pipeline to construct a preference dataset for training hallucination mitigation models. The detection model first identifies hallucinations, then a rewriting model rewrites the hallucinatory response into a non-hallucinatory one.

Hallucination Severity-Aware Direct Preference Optimization (HSA-DPO): The authors introduce a preference learning algorithm that incorporates the hallucination severity scores to prioritize the mitigation of critical hallucinations.

Extensive experiments on hallucination detection and mitigation benchmarks demonstrate the effectiveness of the proposed method, outperforming leading-edge LVLMs and existing hallucination mitigation approaches.

Stats

The average length of responses generated by LLaVA-1.5-13b is 579.2.
The average length of responses generated by LLaVA-1.5-13b with HSA-DPO is 466.8.
The sum of hallucination severity scores for LLaVA-1.5-13b is 42.99.
The sum of hallucination severity scores for LLaVA-1.5-13b with HSA-DPO is 33.50.

Quotes

"Detecting and mitigating hallucinations in LVLMs via fine-grained AI feedback."
"We are the first to use powerful closed-source models to generate fine-grained AI feedback for hallucination detection and mitigation, which is not only effective but also reduces human annotation."
"We propose differentiating the severity of hallucinations, and introducing a Hallucination Severity-Aware Direct Preference Optimization (HSA-DPO) for mitigating hallucination in LVLMs."

Key Insights Distilled From

Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

by Wenyi Xiao,Z... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.14233.pdf

Deeper Inquiries

How can the fine-grained hallucination detection model be further improved to cover more types of hallucinations beyond the primary ones (object, attribute, and relationship)?

To enhance the fine-grained hallucination detection model's coverage of various types of hallucinations, several strategies can be implemented:

Contextual Understanding: Incorporating contextual understanding into the model can help identify more nuanced forms of hallucinations. By analyzing the context surrounding the detected objects, attributes, and relationships, the model can better discern subtle discrepancies that indicate hallucinations.

Semantic Parsing: Introducing semantic parsing techniques can enable the model to extract deeper semantic meaning from the text, allowing it to detect hallucinations related to actions, events, or abstract concepts. This can involve parsing the text into structured representations that capture the relationships between different elements.

Knowledge Integration: Integrating external knowledge bases or ontologies can provide the model with additional information to identify hallucinations related to specific domains or concepts. By leveraging external knowledge sources, the model can enhance its understanding of the world and detect more diverse types of hallucinations.

Multi-Modal Fusion: Incorporating multi-modal fusion techniques that combine information from both text and visual inputs can help detect hallucinations that involve discrepancies between the textual and visual components. By analyzing the alignment between the two modalities, the model can identify inconsistencies that indicate hallucinations.

Adversarial Training: Employing adversarial training methods can help the model learn to detect subtle manipulations or distortions in the generated text that may indicate hallucinations. By training the model to distinguish between genuine descriptions and hallucinated content, it can improve its ability to identify a wider range of hallucination types.

By implementing these strategies and potentially exploring other advanced techniques in natural language processing and computer vision, the fine-grained hallucination detection model can be enhanced to cover a broader spectrum of hallucination types beyond the primary ones.

What are the potential drawbacks or limitations of using closed-source models for generating the fine-grained AI feedback, and how can they be addressed?

Using closed-source models for generating fine-grained AI feedback can offer valuable insights and annotations for training hallucination detection and mitigation models. However, there are potential drawbacks and limitations associated with this approach:

Lack of Transparency: Closed-source models may not provide detailed explanations or transparency regarding how the feedback is generated. This lack of transparency can make it challenging to understand the reasoning behind the detected hallucinations and severity scores.

Limited Customization: Closed-source models are often pre-trained on specific datasets and tasks, which may limit their adaptability to different domains or types of hallucinations. Customizing the feedback generation process to address specific requirements or nuances may be challenging with closed-source models.

Dependency on External Providers: Relying on closed-source models for generating feedback introduces a dependency on external providers, which can lead to issues related to data privacy, availability, and potential changes in the model's behavior over time.

Scalability and Cost: Scaling up the use of closed-source models for generating fine-grained AI feedback can be costly, especially if large amounts of data are required. The licensing fees or usage costs associated with using proprietary models may pose financial challenges.

To address these limitations, several strategies can be considered:

Explainability Techniques: Implementing explainability techniques to interpret the feedback generated by closed-source models can enhance transparency and provide insights into the model's decision-making process.

Transfer Learning: Leveraging transfer learning approaches to fine-tune closed-source models on specific hallucination detection tasks can improve their adaptability and performance in detecting a broader range of hallucination types.

Hybrid Approaches: Combining the outputs of closed-source models with annotations from human experts or other open-access resources can help mitigate the limitations of using proprietary models alone. This hybrid approach can enhance the diversity and quality of the generated feedback.

Model Interpretation: Employing model interpretation techniques to analyze the internal representations of closed-source models can offer insights into how they generate feedback, enabling researchers to better understand and potentially improve the feedback generation process.
By addressing these drawbacks and limitations through a combination of technical approaches and methodological considerations, the use of closed-source models for generating fine-grained AI feedback can be optimized for hallucination detection and mitigation tasks.

How can the proposed framework be extended to other vision-language tasks beyond detailed description generation and visual complex reasoning?

The proposed framework for detecting and mitigating hallucinations in Large Vision Language Models (LVLMs) via fine-grained AI feedback can be extended to various other vision-language tasks by following these steps:

Task Adaptation: Identify the specific requirements and characteristics of the target vision-language task, such as image captioning, visual question answering, or image generation. Understand the types of hallucinations that may occur in these tasks and how they can be detected and mitigated.

Data Collection: Gather a diverse and representative dataset for the target task, including images, textual prompts, and ground truth annotations. Ensure that the dataset covers a wide range of scenarios and challenges relevant to the task.

Fine-Grained AI Feedback: Utilize closed-source models or other advanced AI systems to generate fine-grained AI feedback specific to the new task. Train a hallucination detection model on this feedback to identify hallucinations in the model's outputs accurately.

Preference Dataset Construction: Implement the detect-then-rewrite pipeline to automatically construct a preference dataset for training hallucination mitigation models. Ensure that the dataset captures the nuances and complexities of the target task to effectively mitigate hallucinations.

Hallucination Severity-Aware Optimization: Introduce hallucination severity-aware optimization techniques, similar to the proposed Hallucination Severity-Aware Direct Preference Optimization (HSA-DPO), to prioritize the mitigation of critical hallucinations in the new task.

Evaluation and Validation: Evaluate the performance of the extended framework on benchmark datasets specific to the target vision-language task. Measure the effectiveness of the hallucination detection and mitigation models in improving the fidelity and accuracy of the model's outputs.

By following these steps and customizing the framework to suit the requirements of different vision-language tasks, researchers and practitioners can effectively extend the proposed methodology to address hallucination challenges in a wide range of applications beyond detailed description generation and visual complex reasoning.