Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text
  Analytics? An Examination on Several Typical Tasks
The most recent large language models such as ChatGPT and GPT-4 have garnered
significant attention, as they are capable of generating high-quality responses
to human input. Despite the extensive testing of ChatGPT and GPT-4 on generic
text corpora, showcasing their impressive capabilities, a study focusing on
financial corpora has not been conducted. In this study, we aim to bridge this
gap by examining the potential of ChatGPT and GPT-4 as a solver for typical
financial text analytic problems in the zero-shot or few-shot setting.
Specifically, we assess their capabilities on four representative tasks over
five distinct financial textual datasets. The preliminary study shows that
ChatGPT and GPT-4 struggle on tasks such as financial named entity recognition
(NER) and sentiment analysis, where domain-specific knowledge is required,
while they excel in numerical reasoning tasks. We report both the strengths and
limitations of the current versions of ChatGPT and GPT-4, comparing them to the
state-of-the-art finetuned models as well as pretrained domain-specific
generative models. Our experiments provide qualitative studies, through which
we hope to help understand the capability of the existing models and facilitate
further improvements.
645c446606df9aaa910ee91b
Xianzhi Li
claimed_verified
2023-05-24T15:31:07.146Z
62981a5016e0bb0095ac55ad
/avatars/e5d48bde0505b3985ce7425e71656cea.svg
Jason Lee
user
JXL884
645c446606df9aaa910ee91c
Xiaodan Zhu
645c446606df9aaa910ee91d
Zhiqiang Ma
claimed_verified
2024-01-04T09:33:04.009Z
64b702e8fa7eabaae5f0cbfb
/avatars/512690f60997ae81b79b999a634a1861.svg
Zhiqiang Ma
user
zqmaster
645c446606df9aaa910ee91e
Xiaomo Liu
claimed_verified
2023-05-24T15:31:10.983Z
61b895807b296cc68f98658d
/avatars/ad8af5621af7edb6ba21d0d24455246c.svg
Shawn Liu
user
langqinren
645c446606df9aaa910ee91f
Sameena Shah
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text
  Analytics? An Examination on Several Typical Tasks
The most recent large language models such as ChatGPT and GPT-4 have garnered
significant attention, as they are capable of generating high-quality responses
to human input. Despite the extensive testing of ChatGPT and GPT-4 on generic
text corpora, showcasing their impressive capabilities, a study focusing on
financial corpora has not been conducted. In this study, we aim to bridge this
gap by examining the potential of ChatGPT and GPT-4 as a solver for typical
financial text analytic problems in the zero-shot or few-shot setting.
Specifically, we assess their capabilities on four representative tasks over
five distinct financial textual datasets. The preliminary study shows that
ChatGPT and GPT-4 struggle on tasks such as financial named entity recognition
(NER) and sentiment analysis, where domain-specific knowledge is required,
while they excel in numerical reasoning tasks. We report both the strengths and
limitations of the current versions of ChatGPT and GPT-4, comparing them to the
state-of-the-art finetuned models as well as pretrained domain-specific
generative models. Our experiments provide qualitative studies, through which
we hope to help understand the capability of the existing models and facilitate
further improvements.
ChatGPT and GPT-4 perform well on numerical reasoning tasks in financial texts but struggle with financial NER and sentiment analysis, compared to domain-specific models.
financial named entity recognition (NER) sentiment analysis zero-shot few-shot numerical reasoning state-of-the-art