Analyzing Number Hallucinations in Large Vision-Language Models

Core Concepts

The authors focus on identifying and mitigating number hallucinations in large vision-language models, proposing a consistency training method to address the issue effectively.

Abstract

The study introduces the concept of number hallucination in large vision-language models, highlighting the prevalence of this issue. It explores inner and outer inconsistency problems and proposes a consistency training method to mitigate number hallucinations. Large vision-language models (LVLMs) have shown remarkable efficacy but struggle with various challenges, particularly hallucinations. The study focuses on number hallucination, where models fail to accurately identify object quantities in images. Evaluation metrics reveal severe prevalence of number hallucinations across LVLMs. The authors propose a new form of object hallucination called number hallucination and introduce a dataset for evaluation. They analyze inconsistencies within tasks and between tasks, emphasizing the potential role of inconsistency in contributing to number hallucination. A consistency training method is proposed to alleviate number hallucination by improving model performance through enhanced consistency. Results show an average improvement of 8% compared to direct finetuning methods.

Stats

All LVLMs investigated have an average MAE of around 2 on the dataset. Consistency(I+II) method outperforms Direct by 8% averagely (macro-F1).

Quotes

Key Insights Distilled From

Evaluating and Mitigating Number Hallucinations in Large Vision-Language Models

by Huixuan Zhan... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01373.pdf

Deeper Inquiries

How can the proposed consistency training method be extended to other types of hallucinations?

The proposed consistency training method can be extended to other types of hallucinations by incorporating additional tasks or prompts that target different aspects of the model's understanding. For example, for attribute hallucination, where models may incorrectly identify attributes of objects in an image, additional prompts could focus on comparing attributes between objects or verifying specific attributes present in an image. By introducing these related tasks and ensuring consistency across them, the model can develop a more comprehensive understanding and mitigate attribute hallucination.

What implications does the study's findings have for real-world applications using large vision-language models?

The study's findings have significant implications for real-world applications utilizing large vision-language models. By identifying and addressing number hallucinations through a consistency training method, it enhances the reliability and accuracy of LVLMs in tasks requiring object counting. This improvement is crucial for applications such as visual question answering (VQA) systems used in medical imaging analysis, autonomous driving technology, content moderation in social media platforms, and more. Ensuring that LVLMs accurately interpret visual information can lead to better decision-making processes and outcomes in various industries.

How might addressing inner and outer inconsistency impact overall model performance beyond mitigating number hallucinations?

Addressing inner and outer inconsistency not only helps mitigate number hallucinations but also has broader implications for overall model performance. By improving internal consistency within tasks (inner inconsistency), models develop a clearer understanding of specific concepts or questions, leading to enhanced reasoning abilities. This improved coherence contributes to better generalization capabilities when faced with new data or scenarios. Similarly, tackling outer inconsistency by aligning responses across different perspectives (outer inconsistency) fosters a more holistic comprehension of complex tasks. It encourages models to maintain coherence in their outputs regardless of task format or prompt variations. This alignment promotes robustness and adaptability in handling diverse challenges beyond just counting objects accurately. Overall, addressing both inner and outer inconsistencies enhances the model's cognitive abilities, reasoning skills, and adaptability across various tasks - ultimately elevating its overall performance efficiency and effectiveness in real-world applications requiring nuanced interpretation of visual information.