arXiv:2411.03823v1 Announce Type: cross
Abstract: The rapid progression of multimodal large language models (MLLMs) has demonstrated superior performance on various multimodal benchmarks. However, the issue of data contamination during training creates challenges in performance evaluation and comparison. While numerous methods exist for detecting dataset contamination in large language models (LLMs), they are less effective for MLLMs due to their various modalities and multiple training phases. In this study, we introduce a multimodal data contamination detection framework, MM-Detect, designed for MLLMs. Our experimental results indicate that MM-Detect is sensitive to varying degrees of contamination and can highlight significant performance improvements due to leakage of the training set of multimodal benchmarks. Furthermore, We also explore the possibility of contamination originating from the pre-training phase of LLMs used by MLLMs and the fine-tuning phase of MLLMs, offering new insights into the stages at which contamination may be introduced.
Multi-disciplinary Nature of the Concepts
The content of this article touches upon multiple disciplines, including natural language processing, computer vision, and machine learning. The concept of multimodal large language models (MLLMs) combines textual and visual information, which requires expertise in both language processing and computer vision. The detection of dataset contamination in MLLMs involves methods from machine learning, data analysis, and model evaluation. Therefore, understanding and addressing the challenges presented in this article require a multi-disciplinary approach.
Relation to Multimedia Information Systems
This article’s content is closely related to the field of multimedia information systems, which focuses on the management, retrieval, and analysis of multimedia data. MLLMs, with their ability to process both textual and visual information, align with the goals of multimedia information systems. The detection of dataset contamination in MLLMs contributes to ensuring the quality and reliability of the multimodal data used in such systems. By addressing this issue, researchers and practitioners in multimedia information systems can improve the accuracy and performance of their applications.
Relation to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The concepts discussed in this article have indirect connections to the fields of animations, artificial reality, augmented reality, and virtual realities. While not explicitly mentioned, MLLMs can be utilized in these fields to enhance user experiences by generating more realistic and contextually relevant content. For example, MLLMs can be employed to create more natural dialogue for animated characters or to generate captions for augmented and virtual reality experiences. By understanding and detecting dataset contamination in MLLMs, researchers can ensure that the generated content maintains its quality and aligns with the desired user experiences in these fields.
Expert Insights
The development and application of multimodal large language models have shown substantial progress in various benchmarks. However, the issue of data contamination during training poses challenges in evaluating and comparing the performance of these models. The introduction of the multimodal data contamination detection framework, MM-Detect, tailored specifically for MLLMs is a significant step towards addressing this problem.
The experimental results of MM-Detect demonstrate its sensitivity to different levels of contamination, enabling the identification of significant performance improvements resulting from training set leakage. This helps researchers and practitioners in MLLMs to better understand and mitigate the impact of contaminated data on model performance.
Additionally, the exploration of contamination originating from the pre-training phase of large language models and the fine-tuning phase of MLLMs provides valuable insights into the stages at which data contamination can be introduced. This understanding can guide researchers and developers to implement stricter data quality control measures during these phases, further improving the reliability and efficacy of MLLMs.
In conclusion, the study presented in this article highlights the multi-disciplinary nature of working with multimodal large language models and the challenges associated with data contamination. The proposed multimodal data contamination detection framework and the insights gained from the analysis contribute not only to the field of MLLMs but also to the wider domains of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.