Abstract
The emergence of Large Multimodal Models (LMMs) marks a significant milestone in the development of artificial intelligence. Insurance, as a vast and complex discipline, involves a wide variety of data forms in its operational processes, including text, images, and videos, thereby giving rise to diverse multimodal tasks. Despite this, there has been limited systematic exploration of multimodal tasks specific to insurance, nor a thorough investigation into how LMMs can address these challenges. In this paper, we explore GPT-4V's capabilities in the insurance domain. We categorize multimodal tasks by focusing primarily on visual aspects based on types of insurance (e.g., auto, household/commercial property, health, and agricultural insurance) and insurance stages (e.g., risk assessment, risk monitoring, and claims processing). Our experiment reveals that GPT-4V exhibits remarkable abilities in insurance-related tasks, demonstrating not only a robust understanding of multimodal content in the insurance domain but also a comprehensive knowledge of insurance scenarios. However, there are notable shortcomings: GPT-4V struggles with detailed risk rating and loss assessment, suffers from hallucination in image understanding, and shows variable support for different languages. Through this work, we aim to bridge the insurance domain with cutting-edge LMM technology, facilitate interdisciplinary exchange and development, and provide a foundation for the continued advancement and evolution of future research endeavors.
Abstract (translated)
大规模多模态模型（LMMs）的出现标志着人工智能发展的重要里程碑。作为一门广阔而复杂的学科，保险领域涉及多种数据形式，包括文本、图像和视频，从而产生了各种多模态任务。尽管如此，在保险领域的多模态任务方面，系统性的探索还是有限的，而且关于LMM如何应对这些挑战的研究也是有限的。在本文中，我们探讨了GPT-4V在保险领域的应用能力。我们主要根据保险类型（如汽车、家庭/商业财产、健康和农业保险）对多模态任务进行分类，并关注保险阶段（如风险评估、风险监测和索赔处理）。我们的实验揭示了GPT-4V在保险相关任务中的非凡能力，这不仅表明其在保险领域的多模态内容方面具有稳健的理解，而且表明其在保险场景方面具有全面的了解。然而，仍存在显著的不足：GPT-4V在详细风险评估和损失评估方面表现不佳，在图像理解方面存在幻觉，并且对不同语言的支持具有波动性。通过这项工作，我们旨在将保险领域与最先进的多模态模型技术相连接，促进跨学科的交流和发展，并为未来研究的进步和演变提供基础。
URL
https://arxiv.org/abs/2404.09690
