Study on Evaluation Framework of Large Language Model’s Financial Scenario Capability

With the rapid development of large language models(LLMs),its application in the financial sector has become a dri-ving force for industry transformation.Establishing a standardized and systematic evaluation framework for financial capabilities is a crucial way to assess large language models’ abilities in financial scenarios.However,current evaluation methods have limitations,such as weak generalization of evaluation datasets and narrow coverage of task scenarios.To address these issues,this paper proposes a financial large language model benchmark,named CFBenchmark,which consists of four core assessment modules:financial natural language processing,financial scenario computation,financial analysis and interpretation,and financial compliance and security.High-quality tasks and systematic evaluation metrics are designed based on multi-task scenarios within each module,providing a standardized and systematic approach to assessing large models in the financial domain.Experimental results indicate that the performance of large language models in financial scenarios is closely related to their parameters,architecture,and trai-ning process.As the application of LLMs in the financial sector becomes more widespread in the future,the financial LLM benchmark will need to include more real-world application designs and high-quality evaluation data collection to help enhance the generalization ability of LLMs across diverse financial scenarios.