@inproceedings{li-etal-2023-chatgpt,
title = "Are {C}hat{GPT} and {GPT}-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks",
author = "Li, Xianzhi and
Chan, Samuel and
Zhu, Xiaodan and
Pei, Yulong and
Ma, Zhiqiang and
Liu, Xiaomo and
Shah, Sameena",
editor = "Wang, Mingxuan and
Zitouni, Imed",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-industry.39",
doi = "10.18653/v1/2023.emnlp-industry.39",
pages = "408--422",
abstract = "The most recent large language models (LLMs) such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models, achieving state-of-the-art performance on a wide range of NLP tasks with little or no adaptation. How effective are such models in the finance domain? Understanding this basic question would have a significant impact on many downstream financial analytical tasks. In this paper, we conduct empirical studies and provide experimental evidences of their performance on a wide variety of financial text analytical problems, using eight benchmark datasets from five categories of tasks. We report both the strengths and limitations of the current models by comparing them to the state-of-the-art fine-tuned approaches and the recently released domain-specific pretrained models. We hope our study can help to understand the capability of the existing models in the financial domain and facilitate further improvements.",
}
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%0 Conference Proceedings
%T Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks
%A Li, Xianzhi
%A Chan, Samuel
%A Zhu, Xiaodan
%A Pei, Yulong
%A Ma, Zhiqiang
%A Liu, Xiaomo
%A Shah, Sameena
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-chatgpt
%X The most recent large language models (LLMs) such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models, achieving state-of-the-art performance on a wide range of NLP tasks with little or no adaptation. How effective are such models in the finance domain? Understanding this basic question would have a significant impact on many downstream financial analytical tasks. In this paper, we conduct empirical studies and provide experimental evidences of their performance on a wide variety of financial text analytical problems, using eight benchmark datasets from five categories of tasks. We report both the strengths and limitations of the current models by comparing them to the state-of-the-art fine-tuned approaches and the recently released domain-specific pretrained models. We hope our study can help to understand the capability of the existing models in the financial domain and facilitate further improvements.
%R 10.18653/v1/2023.emnlp-industry.39
%U https://aclanthology.org/2023.emnlp-industry.39
%U https://doi.org/10.18653/v1/2023.emnlp-industry.39
%P 408-422
Markdown (Informal)
[Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks](https://aclanthology.org/2023.emnlp-industry.39) (Li et al., EMNLP 2023)
ACL