INSEva: A Comprehensive Chinese Benchmark for Large Language Models in Insurance

• URL: http://arxiv.org/abs/2509.04455v1
• Date: Wed, 27 Aug 2025 03:13:40 GMT
• Title: INSEva: A Comprehensive Chinese Benchmark for Large Language Models in Insurance
• Authors: Shisong Chen, Qian Zhu, Wenyan Yang, Chengyi Yang, Zhong Wang, Ping Wang, Xuan Lin, Bo Xu, Daqian Li, Chao Yuan, Licai Qi, Wanqing Xu, sun zhenxing, Xin Lu, Shiqiang Xiong, Chao Chen, Haixiang Hu, Yanghua Xiao,
• Abstract summary: INSEva is a Chinese benchmark specifically designed for evaluating AI systems' knowledge and capabilities in insurance.<n> INSEva features a multi-dimensional evaluation taxonomy covering business areas, task formats, difficulty levels, and cognitive-knowledge dimension.<n>Our benchmark implements tailored evaluation methods for assessing both faithfulness and completeness in open-ended responses.
• Score: 48.22571187209047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Insurance, as a critical component of the global financial system, demands high standards of accuracy and reliability in AI applications. While existing benchmarks evaluate AI capabilities across various domains, they often fail to capture the unique characteristics and requirements of the insurance domain. To address this gap, we present INSEva, a comprehensive Chinese benchmark specifically designed for evaluating AI systems' knowledge and capabilities in insurance. INSEva features a multi-dimensional evaluation taxonomy covering business areas, task formats, difficulty levels, and cognitive-knowledge dimension, comprising 38,704 high-quality evaluation examples sourced from authoritative materials. Our benchmark implements tailored evaluation methods for assessing both faithfulness and completeness in open-ended responses. Through extensive evaluation of 8 state-of-the-art Large Language Models (LLMs), we identify significant performance variations across different dimensions. While general LLMs demonstrate basic insurance domain competency with average scores above 80, substantial gaps remain in handling complex, real-world insurance scenarios. The benchmark will be public soon.

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