arXiv:2504.16936v1 Announce Type: new
Abstract: Multi-modal large language models (MLLMs) have recently achieved great success in processing and understanding information from diverse modalities (e.g., text, audio, and visual signals). Despite their growing popularity, there remains a lack of comprehensive evaluation measuring the audio-visual capabilities of these models, especially in diverse scenarios (e.g., distribution shifts and adversarial attacks). In this paper, we present a multifaceted evaluation of the audio-visual capability of MLLMs, focusing on four key dimensions: effectiveness, efficiency, generalizability, and robustness. Through extensive experiments, we find that MLLMs exhibit strong zero-shot and few-shot generalization abilities, enabling them to achieve great performance with limited data. However, their success relies heavily on the vision modality, which impairs performance when visual input is corrupted or missing. Additionally, while MLLMs are susceptible to adversarial samples, they demonstrate greater robustness compared to traditional models. The experimental results and our findings provide insights into the audio-visual capabilities of MLLMs, highlighting areas for improvement and offering guidance for future research.
Expert Commentary: Evaluating the Audio-Visual Capabilities of Multi-Modal Large Language Models
In recent years, multi-modal large language models (MLLMs) have gained significant attention and achieved remarkable success in processing and understanding information from various modalities such as text, audio, and visual signals. However, despite their widespread use, there has been a lack of comprehensive evaluation measuring the audio-visual capabilities of these models across diverse scenarios.
This paper fills this knowledge gap by presenting a multifaceted evaluation of MLLMs’ audio-visual capabilities, focusing on four key dimensions: effectiveness, efficiency, generalizability, and robustness. These dimensions encompass different aspects that are crucial for assessing the overall performance and potential limitations of MLLMs in processing audio-visual data.
Effectiveness refers to how well MLLMs can accurately process and understand audio-visual information. The experiments conducted in this study reveal that MLLMs demonstrate strong zero-shot and few-shot generalization abilities. This means that even with limited data or completely new examples, they can still achieve impressive performance. This finding highlights the potential of MLLMs in handling tasks that require quick adaptation to new scenarios or concepts, making them highly flexible and versatile.
Efficiency is another important aspect evaluated in the study. Although MLLMs excel in effectiveness, their computational efficiency needs attention. Given their large size and complexity, MLLMs tend to be computationally intensive, which can pose challenges in real-time applications or systems with limited computational resources. Further research and optimization techniques are required to enhance their efficiency without sacrificing performance.
Generalizability is a critical factor in assessing the practical usability of MLLMs. The results indicate that MLLMs heavily rely on the vision modality, and their performance suffers when visual input is corrupted or missing. This limitation implies that MLLMs may not be suitable for tasks where visual information is unreliable or incomplete, such as in scenarios with noisy or degraded visual signals. Addressing this issue is crucial to improve the robustness and generalizability of MLLMs across diverse real-world situations.
Lastly, the study explores the robustness of MLLMs against adversarial attacks. Adversarial attacks attempt to deceive or mislead the model by introducing subtly crafted perturbations to the input data. While MLLMs are not immune to these attacks, they exhibit greater robustness compared to traditional models. This finding suggests that MLLMs have inherent built-in defenses against adversarial attacks, which opens up possibilities for leveraging their robustness and security features.
From a broader perspective, this research is highly relevant to the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The evaluation of MLLMs’ audio-visual capabilities contributes to our understanding of how these models can be effectively utilized in multimedia processing, including tasks like video captioning, content understanding, and interactive virtual environments. The findings also shed light on the interdisciplinary nature of MLLMs, as they demonstrate the fusion and interplay of language understanding, computer vision, and audio processing.
In conclusion, this paper provides a comprehensive evaluation of the audio-visual capabilities of multi-modal large language models. The findings offer valuable insights into the strengths and limitations of these models, paving the way for future improvements and guiding further research towards enhancing the effectiveness, efficiency, generalizability, and robustness of MLLMs in processing and understanding multi-modal information.