Evaluating Large Vision-Language Models: Identifying Limitations and Proposing a Robust Benchmark

核心概念

Large vision-language models (LVLMs) have recently achieved rapid progress, but current evaluation methods have two primary issues: 1) Many evaluation samples do not require visual understanding, as the answers can be directly inferred from the questions and options or the world knowledge embedded in language models. 2) Unintentional data leakage exists in the training of LLMs and LVLMs, allowing them to answer some visual-necessary questions without accessing the images.

摘要

The paper identifies two key issues in the current evaluation of large vision-language models (LVLMs):

1. Visual content is unnecessary for many evaluation samples:

   • Some samples have answers that can be directly inferred from the questions and options, without requiring visual understanding.
   • Other samples can be answered using the world knowledge embedded in large language models (LLMs), without needing the visual input.
   • Quantitative analysis shows that a significant portion of samples across popular benchmarks exhibit this issue, with some benchmarks having over 50% of samples that can be solved by LLMs without visual input.

2. Unintentional data leakage exists in the training of LLMs and LVLMs:

   • LLMs and LVLMs can sometimes answer visual-necessary questions without accessing the images, suggesting they have memorized these samples during the large-scale training process.
   • Detailed experiments show that this data leakage problem is particularly serious for LVLMs, with some models outperforming their LLM backbones on certain benchmarks without using visual input.

To address these issues, the authors introduce the MMStar benchmark, a new elite vision-critical multi-modal benchmark with 1,500 carefully curated samples. MMStar covers 6 core capabilities and 18 detailed axes, aiming to evaluate the actual multi-modal capabilities of LVLMs. Additionally, the authors propose two new metrics, multi-modal gain (MG) and multi-modal leakage (ML), to measure the actual performance gain and data leakage degree in multi-modal training.

Experiments on MMStar and other benchmarks show that the high-resolution version of GPT-4V outperforms 16 leading LLMs and LVLMs, ranking first with 57.1% accuracy. GPT-4V also achieves the best MG and a small ML, indicating its effective multi-modal training strategy and less data leakage.

統計資料

GeminiPro achieves 42.9% on the MMMU benchmark without any visual input, outperforming the random choice baseline across six benchmarks by over 20% on average.
Sphinx-X-MoE gets 43.6% on MMMU without accessing images, surpassing its LLM backbone with 17.9%.

引述

"Visual content is unnecessary for many samples. The answers can be directly inferred from the questions and options, or the world knowledge embedded in LLMs."
"Unintentional data leakage exists in LLM and LVLM training. LLM and LVLM could still answer some visual-necessary questions without visual content, indicating the memorizing of these samples within large-scale training data."

從以下內容提煉的關鍵洞見

Are We on the Right Way for Evaluating Large Vision-Language Models?

by Lin Chen,Jin... 於 arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.20330.pdf

深入探究

How can the research community ensure that multi-modal benchmarks truly evaluate the integrated understanding of visual and textual information, rather than just the memorization of specific samples?

To ensure that multi-modal benchmarks accurately evaluate the integrated understanding of visual and textual information, the research community can implement several strategies:

Curate Diverse and Challenging Samples: It is crucial to select samples that require a deep comprehension of both visual and textual inputs to arrive at the correct answer. By including diverse and challenging samples that necessitate the integration of both modalities, benchmarks can effectively assess the multi-modal capabilities of models.

Human Review Process: Incorporating a human review process can help identify samples that may lead to memorization or data leakage. Human reviewers can ensure that each sample truly requires the model to understand and reason with both visual and textual information, rather than relying on memorized responses.

Balanced Distribution of Difficulty Levels: Benchmarks should include samples across various difficulty levels to test the model's ability to handle a wide range of multi-modal tasks. This balanced distribution ensures that models are evaluated on their true understanding and reasoning capabilities, rather than their capacity to memorize specific samples.

Metrics for Data Leakage: Introducing metrics to quantify data leakage can help in identifying samples that may have inadvertently leaked into the training data. By measuring the performance gain of models with and without visual inputs, researchers can assess the extent of data leakage and adjust benchmarks accordingly.

Continuous Evaluation and Improvement: Regularly updating benchmarks based on feedback and insights from the research community can help in refining the evaluation process. By continuously improving benchmarks to reflect the evolving capabilities of models, researchers can ensure a more accurate assessment of multi-modal understanding.

How can the research community ensure that multi-modal benchmarks truly evaluate the integrated understanding of visual and textual information, rather than just the memorization of specific samples?

To ensure that multi-modal benchmarks accurately evaluate the integrated understanding of visual and textual information, the research community can implement several strategies:

Curate Diverse and Challenging Samples: It is crucial to select samples that require a deep comprehension of both visual and textual inputs to arrive at the correct answer. By including diverse and challenging samples that necessitate the integration of both modalities, benchmarks can effectively assess the multi-modal capabilities of models.

Human Review Process: Incorporating a human review process can help identify samples that may lead to memorization or data leakage. Human reviewers can ensure that each sample truly requires the model to understand and reason with both visual and textual information, rather than relying on memorized responses.

Balanced Distribution of Difficulty Levels: Benchmarks should include samples across various difficulty levels to test the model's ability to handle a wide range of multi-modal tasks. This balanced distribution ensures that models are evaluated on their true understanding and reasoning capabilities, rather than their capacity to memorize specific samples.

Metrics for Data Leakage: Introducing metrics to quantify data leakage can help in identifying samples that may have inadvertently leaked into the training data. By measuring the performance gain of models with and without visual inputs, researchers can assess the extent of data leakage and adjust benchmarks accordingly.

Continuous Evaluation and Improvement: Regularly updating benchmarks based on feedback and insights from the research community can help in refining the evaluation process. By continuously improving benchmarks to reflect the evolving capabilities of models, researchers can ensure a more accurate assessment of multi-modal understanding.

What other potential sources of bias or leakage might exist in the training data and evaluation processes for large vision-language models, and how can they be identified and mitigated?

In addition to the inadvertent memorization of specific samples, other potential sources of bias or leakage in the training data and evaluation processes for large vision-language models include:

Dataset Biases: Training data may contain biases related to demographics, cultural references, or societal stereotypes, leading to biased model predictions. These biases can impact the model's performance on certain groups or topics and should be identified and mitigated through careful dataset curation and bias detection techniques.

Domain-Specific Leakage: Models may inadvertently memorize domain-specific information during training, leading to inflated performance on certain tasks. To mitigate this, researchers can introduce domain adaptation techniques or domain-specific regularization to prevent overfitting to specific domains.

Evaluation Set Leakage: Evaluation sets that overlap with the training data can result in inflated performance metrics, as models may have seen similar samples during training. Cross-validation techniques and ensuring strict separation between training and evaluation data can help mitigate this form of leakage.

Adversarial Attacks: Adversarial examples designed to exploit model vulnerabilities can introduce biases or leakage in the evaluation process. Robustness testing and adversarial training can help identify and mitigate the impact of such attacks on model performance.

Data Augmentation Biases: Biases introduced during data augmentation techniques can affect model generalization and performance. Researchers should carefully monitor the impact of data augmentation on model behavior and adjust augmentation strategies to minimize biases.

Identifying and mitigating these sources of bias and leakage requires a combination of rigorous dataset curation, robust evaluation methodologies, and ongoing monitoring of model performance across diverse datasets and tasks.

Given the rapid progress in large language models and the increasing integration of visual modalities, what new frontiers or applications might emerge for these powerful multi-modal systems in the future?

The rapid progress in large language models and the integration of visual modalities open up exciting new frontiers and applications for multi-modal systems in the future:

Enhanced Human-Computer Interaction: Multi-modal systems can revolutionize human-computer interaction by enabling more natural and intuitive communication. Applications in virtual assistants, chatbots, and interactive interfaces can benefit from the combined understanding of visual and textual inputs for more contextually relevant responses.

Personalized Content Generation: Multi-modal systems can be leveraged for personalized content generation in areas such as content creation, storytelling, and multimedia production. By understanding both visual and textual cues, these systems can tailor content to individual preferences and requirements.

Medical Imaging and Diagnosis: Integration of visual modalities in large language models can significantly impact medical imaging and diagnosis. Multi-modal systems can assist healthcare professionals in analyzing medical images, interpreting diagnostic reports, and providing personalized treatment recommendations.

Autonomous Vehicles and Robotics: Multi-modal systems can enhance the perception and decision-making capabilities of autonomous vehicles and robots. By integrating visual and textual information, these systems can navigate complex environments, interpret signals, and interact with users more effectively.

Education and Training: Multi-modal systems can transform the education sector by providing interactive and personalized learning experiences. These systems can offer visual explanations, interactive simulations, and real-time feedback to enhance student engagement and understanding.

Art and Creativity: Multi-modal systems can be used in creative fields such as art, design, and music composition. By combining visual and textual inputs, these systems can assist artists in generating innovative ideas, designing visual concepts, and creating multimedia artworks.

Overall, the integration of visual modalities in large language models opens up a wide range of possibilities for innovative applications across various industries and domains, paving the way for more sophisticated and interactive AI systems in the future.