“LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models” presents a critical examination of the current evaluation practices for large multimodal models (LMMs). This research addresses the growing concern that existing evaluation methodologies may not adequately capture the true capabilities and limitations of LMMs, which are increasingly being used in diverse applications such as image captioning, visual question answering, and cross-modal retrieval.
The core objective of this study is to provide a comprehensive analysis of the evaluation metrics and benchmarks commonly used to assess LMMs. The authors argue that many of these metrics fail to account for the complexities and nuances of multimodal tasks, leading to an incomplete or even misleading understanding of model performance. To address this, the paper introduces LMMs-Eval, a framework designed to offer a more robust and nuanced evaluation of LMMs.
LMMs-Eval focuses on several key aspects of evaluation. First, it emphasizes the importance of context in multimodal tasks, highlighting that models should be assessed on their ability to understand and integrate information across different modalities in a contextually appropriate manner. Second, the framework advocates for the use of diverse and challenging benchmarks that better reflect real-world scenarios. This includes incorporating datasets with varied and complex inputs, as well as tasks that require higher-order reasoning and cross-modal understanding.
The paper provides extensive experimental results to demonstrate the shortcomings of current evaluation practices and the advantages of the proposed LMMs-Eval framework. The authors conduct a series of experiments using several state-of-the-art LMMs, comparing their performance under traditional evaluation metrics and the new framework. The results reveal significant discrepancies, with LMMs-Eval providing a more accurate and comprehensive assessment of model capabilities.
Additionally, the paper includes qualitative examples that illustrate the practical implications of using LMMs-Eval. These examples show how the framework can identify specific strengths and weaknesses of LMMs that are overlooked by traditional metrics. This makes LMMs-Eval a valuable tool for researchers and practitioners aiming to develop and deploy more effective multimodal models. “LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models” offers a significant contribution to the field of multimodal AI by proposing a more rigorous and context-aware evaluation framework. By addressing the limitations of current evaluation practices, the authors provide a pathway for more accurate and meaningful assessments of LMMs, ultimately enhancing their development and application in real-world scenarios.
