title: Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text
    Analytics? An Examination on Several Typical Tasks
summary: 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.
paper_authors._id: 645c446606df9aaa910ee91b
paper_authors.name: Xianzhi Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-24T15:31:07.146Z
paper_authors._id: 62981a5016e0bb0095ac55ad
paper_authors.avatarUrl: /avatars/e5d48bde0505b3985ce7425e71656cea.svg
paper_authors.fullname: Jason Lee
paper_authors.type: user
paper_authors.user: JXL884
paper_authors._id: 645c446606df9aaa910ee91c
paper_authors.name: Xiaodan Zhu
paper_authors._id: 645c446606df9aaa910ee91d
paper_authors.name: Zhiqiang Ma
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-04T09:33:04.009Z
paper_authors._id: 64b702e8fa7eabaae5f0cbfb
paper_authors.avatarUrl: /avatars/512690f60997ae81b79b999a634a1861.svg
paper_authors.fullname: Zhiqiang Ma
paper_authors.type: user
paper_authors.user: zqmaster
paper_authors._id: 645c446606df9aaa910ee91e
paper_authors.name: Xiaomo Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-24T15:31:10.983Z
paper_authors._id: 61b895807b296cc68f98658d
paper_authors.avatarUrl: /avatars/ad8af5621af7edb6ba21d0d24455246c.svg
paper_authors.fullname: Shawn Liu
paper_authors.type: user
paper_authors.user: langqinren
paper_authors._id: 645c446606df9aaa910ee91f
paper_authors.name: Sameena Shah
paper_title: Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text
    Analytics? An Examination on Several Typical Tasks
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: financial named entity recognition (NER) sentiment analysis zero-shot few-shot numerical reasoning state-of-the-art