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.
financial named entity recognition (NER) sentiment analysis numerical reasoning tasks zero-shot few-shot setting ChatGPT GPT-4 state-of-the-art finetuned models pretrained domain-specific generative models