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The most recent large language models(LLMs) such as ChatGPT and GPT-4 haveshown exceptional capabilities of generalist models, achieving state-of-the-artperformance on a wide range of NLP tasks with little or no adaptation. Howeffective are such models in the financial domain? Understanding this basicquestion would have a significant impact on many downstream financialanalytical tasks. In this paper, we conduct an empirical study and provideexperimental evidences of their performance on a wide variety of financial textanalytical problems, using eight benchmark datasets from five categories oftasks. We report both the strengths and limitations of the current models bycomparing them to the state-of-the-art fine-tuned approaches and the recentlyreleased domain-specific pretrained models. We hope our study can helpunderstand the capability of the existing models in the financial domain andfacilitate further improvements.

Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks