Reproduce “SuperCLUE-Fin: Graded Fine-Grained Analysis of Chinese LLMs on Diverse Financial Tasks and Applications” on OpenResearch
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About this paper
Liang Xu, Lei Zhu, Yaotong Wu, Hang Xue
SuperCLUE-Fin introduces a benchmark for evaluating Chinese-native financial large language models (FLMs), comprising over 1000 questions across six financial domains and 25 tasks, assessed through a human-reviewed multi-turn Q&A system. The evaluation revealed that leading models, including domestic Chinese FLMs, exhibit strong foundational financial knowledge but show a noticeable gap in applying this knowledge to complex real-world investment research and advisory scenarios.
Key insights
- While financial LLMs, including leading domestic Chinese models, demonstrate strong foundational financial knowledge (Basic Capabilities), they exhibit a notable deficiency in applying this knowledge to complex, real-world financial scenarios, particularly in investment research and advisory.
- Performance in financial fundamentals shows a high correlation with mathematical calculation and logical analysis abilities, indicating the importance of a robust knowledge base for quantitative reasoning.
- The benchmark highlights areas where current FLMs struggle, such as financial information extraction and analysis, and providing specific, in-depth investment advice, suggesting targets for future model improvement.
Summary by alphaXiv.
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