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The emergence of Large Language Models (LLMs), such as ChatGPT, hasrevolutionized general natural language preprocessing (NLP) tasks. However,their expertise in the financial domain lacks a comprehensive evaluation. Toassess the ability of LLMs to solve financial NLP tasks, we present FinLMEval,a framework for Financial Language Model Evaluation, comprising nine datasetsdesigned to evaluate the performance of language models. This study comparesthe performance of encoder-only language models and the decoder-only languagemodels. Our findings reveal that while some decoder-only LLMs demonstratenotable performance across most financial tasks via zero-shot prompting, theygenerally lag behind the fine-tuned expert models, especially when dealing withproprietary datasets. We hope this study provides foundation evaluations forcontinuing efforts to build more advanced LLMs in the financial domain.

Is ChatGPT a Financial Expert? Evaluating Language Models on Financial Natural Language Processing