arXiv:2405.07229v1 Announce Type: new
Abstract: The rising popularity of multimodal large language models (MLLMs) has sparked a significant increase in research dedicated to evaluating these models. However, current evaluation studies predominantly concentrate on the ability of models to comprehend and reason within a unimodal (vision-only) context, overlooking critical performance evaluations in complex multimodal reasoning tasks that integrate both visual and text contexts. Furthermore, tasks that demand reasoning across multiple modalities pose greater challenges and require a deep understanding of multimodal contexts. In this paper, we introduce a comprehensive assessment framework named MM-InstructEval, which integrates a diverse array of metrics to provide an extensive evaluation of the performance of various models and instructions across a broad range of multimodal reasoning tasks with vision-text contexts. MM-InstructEval enhances the research on the performance of MLLMs in complex multimodal reasoning tasks, facilitating a more thorough and holistic zero-shot evaluation of MLLMs. We firstly utilize the “Best Performance” metric to determine the upper performance limit of each model across various datasets. The “Mean Relative Gain” metric provides an analysis of the overall performance across different models and instructions, while the “Stability” metric evaluates their sensitivity to variations. Historically, the research has focused on evaluating models independently or solely assessing instructions, overlooking the interplay between models and instructions. To address this gap, we introduce the “Adaptability” metric, designed to quantify the degree of adaptability between models and instructions. Evaluations are conducted on 31 models (23 MLLMs) across 16 multimodal datasets, covering 6 tasks, with 10 distinct instructions. The extensive analysis enables us to derive novel insights.
As the field of multimodal large language models (MLLMs) continues to advance, there is a growing need for comprehensive evaluation frameworks that can assess the performance of these models in complex multimodal reasoning tasks. The MM-InstructEval framework introduced in this paper aims to fill this gap by providing a diverse set of metrics to evaluate the performance of MLLMs across a broad range of tasks that integrate both visual and text contexts.
Multi-disciplinary Nature
The concepts discussed in this paper have a multi-disciplinary nature, spanning multiple fields such as natural language processing, computer vision, and human-computer interaction. By evaluating the performance of MLLMs in multimodal reasoning tasks, this research contributes to the development of more advanced and comprehensive multimedia information systems. These systems can utilize both textual and visual information to facilitate better understanding, decision-making, and interaction between humans and machines.
Related to Multimedia Information Systems
The MM-InstructEval framework is directly related to the field of multimedia information systems. These systems deal with the retrieval, management, and analysis of multimedia data, including text, images, and videos. By evaluating the performance of MLLMs in multimodal reasoning tasks, this framework enables the development of more effective multimedia information systems that can understand and reason over diverse modalities of data, improving the accuracy and usefulness of information retrieval and analysis tasks.
Related to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The evaluation of MLLMs in multimodal reasoning tasks has implications for various aspects of animations, artificial reality, augmented reality, and virtual realities. These technologies often rely on both visual and textual information to create immersive and interactive experiences. By improving the performance of MLLMs in understanding and reasoning across multimodal contexts, the MM-InstructEval framework can enhance the quality and realism of animations, artificial reality simulations, and augmented reality applications. It can also enable more intelligent virtual reality environments that can understand and respond to user instructions and queries more accurately and effectively.
Novel Insights from the Evaluation
The extensive analysis conducted using the MM-InstructEval framework on 31 models across 16 multimodal datasets and 6 tasks provides novel insights into the performance of MLLMs in complex reasoning tasks. The “Best Performance” metric helps determine the upper performance limit of each model, giving a baseline for comparison. The “Mean Relative Gain” metric provides an overall analysis of performance across different models and instructions, highlighting the strengths and weaknesses of each. The “Stability” metric evaluates the models’ sensitivity to variations, ensuring robustness. Lastly, the “Adaptability” metric measures the degree of adaptability between models and instructions, shedding light on the interplay between them.
By considering these metrics and conducting a comprehensive evaluation, researchers and developers can better understand the capabilities and limitations of MLLMs in multimodal reasoning tasks. This knowledge can inform the development of more advanced MLLMs, as well as the design and implementation of multimedia information systems, animations, artificial reality experiences, augmented reality applications, and virtual reality environments.