Summary of Harnessing Gpt-4v(ision) For Insurance: a Preliminary Exploration, by Chenwei Lin et al.
Harnessing GPT-4V(ision) for Insurance: A Preliminary Exploration
by Chenwei Lin, Hanjia Lyu, Jiebo Luo, Xian Xu
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the capabilities of Large Multimodal Models (LMMs) in the insurance domain. Insurance involves various data forms, including text, images, and videos, giving rise to diverse multimodal tasks. Despite this complexity, there has been limited investigation into how LMMs can address these challenges. The authors categorize multimodal tasks based on types of insurance and stages, such as risk assessment and claims processing. They find that GPT-4V exhibits remarkable abilities in insurance-related tasks, demonstrating a robust understanding of multimodal content and 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. This work aims to bridge the insurance domain with cutting-edge LMM technology, facilitating interdisciplinary exchange and development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence can help with insurance tasks. Insurance is a big and complex field that involves lots of different types of data, like text, images, and videos. The researchers want to see if special kinds of AI models called Large Multimodal Models (LMMs) can help with insurance tasks. They found that these models are good at understanding and working with this type of data, but they still have some problems, like being bad at rating risks or understanding images. This research is important because it helps bridge the gap between technology and the insurance industry. |
Keywords
» Artificial intelligence » Gpt » Hallucination
