Mitigating Hallucinations in Large Vision-Language Models through Targeted Instruction Tuning

Core Concepts

Hallucinations in large vision-language models can be effectively mitigated by generating targeted instruction data that accounts for the hallucination specificity of different models.

Abstract

The paper investigates the hallucination specificity of large vision-language models (LVLMs), where different models exhibit varied hallucination patterns for the same image. It is found that existing instruction tuning datasets, such as LRV-Instruction, do not consider this hallucination specificity, thereby diminishing their effectiveness in mitigating model hallucinations.
To address this issue, the authors propose the DFTG (Diagnose First, Then Generate) framework. DFTG first diagnoses the hallucinations of a given LVLM on an image by extracting textual and visual information, and then generates targeted instruction data based on the diagnostic results. The experimental results on hallucination benchmarks demonstrate that the targeted instruction data generated by DFTG are more effective in mitigating hallucinations compared to previous datasets.
The key steps of the DFTG framework are:

Caption generation: Obtain the model's description of the image to understand its perception.
Text information extraction: Extract key objects, attributes, and quantities mentioned in the description.
Image information extraction: Detect the actual objects, attributes, and quantities present in the image using an open-vocabulary object detection model.
Hallucination checking: Compare the textual and visual information to identify hallucinations.
Targeted instruction data generation: Generate positive and negative instruction samples based on the diagnostic results, covering object existence, attributes, position, and relations.

The experimental results on POPE, MME, AMBER, and VHTest datasets show that the models fine-tuned with the targeted instruction data generated by DFTG outperform the original models and those fine-tuned with existing instruction datasets in mitigating hallucinations.

Stats

The scene features a small, white airplane parked on an airport runway.
There is a person visible near the airplane, possibly an airport worker or a pilot.
In addition to the airplane and person, there is a truck parked in the background.

Quotes

None

Key Insights Distilled From

Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning

by Rui Hu,Yahan... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10332.pdf

Deeper Inquiries

How can the DFTG framework be extended to diagnose and mitigate more complex types of hallucinations, such as those involving actions and relationships?

In order to extend the DFTG framework to diagnose and mitigate more complex types of hallucinations, such as those involving actions and relationships, several enhancements can be implemented:

Action Recognition: Integrate action recognition models to identify actions depicted in images. By incorporating models trained specifically for action recognition, the framework can extract information about actions performed in the scene. This information can then be compared with the model's generated responses to identify hallucinations related to actions.

Relation Extraction: Implement relation extraction techniques to identify relationships between objects in the image. By analyzing the spatial and contextual relationships between objects, the framework can detect hallucinations related to incorrect object interactions or associations.

Semantic Parsing: Utilize semantic parsing techniques to extract complex relationships and events from textual descriptions. By parsing the text for semantic structures and dependencies, the framework can identify discrepancies between the model's understanding of relationships and the actual content of the image.

Multi-modal Fusion: Employ advanced multi-modal fusion methods to combine information from both visual and textual modalities. By integrating features from both modalities effectively, the framework can capture nuanced relationships and actions depicted in the image, enabling more accurate diagnosis of complex hallucinations.

By incorporating these enhancements, the DFTG framework can broaden its scope to diagnose and mitigate a wider range of hallucinations, including those involving actions and relationships, in large vision-language models.

What are the potential limitations of the current information extraction methods used in the DFTG framework, and how can they be improved to enhance the quality of the generated instruction data?

The current information extraction methods used in the DFTG framework may have the following limitations:

Accuracy: The accuracy of the information extracted from images and textual descriptions can impact the quality of the generated instruction data. Inaccuracies in object detection or entity extraction can lead to misdiagnosis of hallucinations.

Coverage: The scope of information extraction may be limited, potentially missing crucial details that could aid in hallucination diagnosis. Incomplete coverage of objects, attributes, or relationships can result in incomplete or inaccurate instruction data.

Robustness: The robustness of the information extraction models to variations in image quality, lighting conditions, or textual complexity may affect the reliability of the extracted information. Lack of robustness can lead to inconsistencies in the generated instruction data.

To enhance the quality of the generated instruction data, the following improvements can be considered:

Fine-tuning Information Extraction Models: Fine-tune object detection and entity extraction models on domain-specific data to improve accuracy and coverage. Training these models on a diverse range of images and textual data can enhance their performance.

Ensemble Methods: Implement ensemble methods that combine outputs from multiple information extraction models to improve robustness and accuracy. By aggregating predictions from different models, the framework can mitigate errors and enhance the quality of extracted information.

Feedback Mechanisms: Incorporate feedback mechanisms to iteratively improve information extraction. By incorporating user feedback or self-correcting mechanisms, the framework can continuously learn and adapt to enhance the quality of extracted information over time.

By addressing these limitations and implementing the suggested improvements, the DFTG framework can enhance the quality and reliability of the generated instruction data for mitigating hallucinations in large vision-language models.

How can the targeted instruction data generation process be further optimized to better capture the unique hallucination patterns of different LVLMs, potentially through the use of more advanced machine learning techniques?

To optimize the targeted instruction data generation process and better capture the unique hallucination patterns of different LVLMs, advanced machine learning techniques can be leveraged:

Adversarial Training: Implement adversarial training techniques to generate targeted instruction data that specifically challenge the model's hallucination patterns. By training the framework against adversarial examples that exploit the model's weaknesses, the generated data can better target and correct hallucinations.

Meta-Learning: Utilize meta-learning approaches to adapt the instruction generation process to the specific characteristics of each LVLM. By meta-learning the generation process on a diverse set of LVLMs, the framework can learn to tailor instruction data to individual model behaviors and hallucination patterns.

Generative Models: Employ generative models, such as variational autoencoders or generative adversarial networks, to generate diverse and realistic instruction data. By training generative models on a large corpus of image-text pairs, the framework can produce targeted instructions that effectively address hallucinations in different LVLMs.

Reinforcement Learning: Explore reinforcement learning techniques to optimize the generation process iteratively. By rewarding the framework for generating instruction data that successfully mitigates hallucinations, reinforcement learning can guide the generation process towards more effective and targeted data creation.

By integrating these advanced machine learning techniques into the targeted instruction data generation process, the DFTG framework can be further optimized to capture the unique hallucination patterns of different LVLMs, leading to more precise and tailored instruction data for mitigating model hallucinations.