Summary of Ins-mmbench: a Comprehensive Benchmark For Evaluating Lvlms’ Performance in Insurance, by Chenwei Lin et al.
INS-MMBench: A Comprehensive Benchmark for Evaluating LVLMs’ Performance in Insurance
by Chenwei Lin, Hanjia Lyu, Xian Xu, Jiebo Luo
First submitted to arxiv on: 13 Jun 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 A novel paper explores the potential of Large Vision-Language Models (LVLMs) in the insurance domain, a previously underserved area. The authors systematically review and distill multimodal tasks for four types of insurance: auto, property, health, and agricultural. They propose INS-MMBench, a comprehensive benchmark tailored to evaluate LVLMs’ capabilities in this domain. The benchmark consists of 2.2K multiple-choice questions, covering 12 meta-tasks and 22 fundamental tasks. Additionally, the authors evaluate various LVLMs, including GPT-4o and BLIP-2, validating the effectiveness of their benchmark and providing insights into current models’ performance on insurance-related multimodal tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Insurance companies are exploring new ways to use Large Vision-Language Models (LVLMs) to process large amounts of data. Right now, there isn’t a good way to test how well LVLMs work in the insurance industry. This paper tries to fill that gap by creating a special benchmark for testing LVLMs in four types of insurance: auto, property, health, and agricultural. The authors also tested different LVLMs on this benchmark to see which ones work best. This research could help make LVLMs more useful in the insurance industry. |
Keywords
» Artificial intelligence » Gpt
