# Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? An Examination on Several Typical Tasks

**Authors:** Xianzhi Li, Xiaodan Zhu, Zhiqiang Ma, Xiaomo Liu, Sameena Shah  
**arXiv:** [2305.05862](https://arxiv.org/abs/2305.05862)  
**Date:** 2023-05-10  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.05862v1/

## Key points

- Study examines potential of ChatGPT and GPT-4 in solving financial text analytic problems
- Models have been extensively tested on generic text corpora but not on financial corpora
- Preliminary study assesses capabilities of ChatGPT and GPT-4 on four representative tasks using five financial textual datasets
- Models excel in numerical reasoning tasks but struggle with financial named entity recognition (NER) and sentiment analysis
- Study compares strengths and limitations of ChatGPT and GPT-4 with finetuned models and domain-specific generative models
- Qualitative experiments provide insights into existing models' capabilities
- Research sheds light on applicability of large language models in solving financial text analytic problems
- Identifies strengths and weaknesses of ChatGPT and GPT-4 for future advancements in leveraging these models for financial analysis.

## Summary

.In their study titled "Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? An Examination on Several Typical Tasks," Xianzhi Li, Xiaodan Zhu, Zhiqiang Ma, Xiaomo Liu, and Sameena Shah explore the potential of large language models such as ChatGPT and GPT-4 in solving financial text analytic problems. While these models have been extensively tested on generic text corpora and proven to generate high-quality responses, their performance on financial corpora has not been thoroughly examined. To bridge this gap, the authors conduct a preliminary study to assess the capabilities of ChatGPT and GPT-4 in a zero-shot or few-shot setting on four representative tasks using five distinct financial textual datasets. The results reveal that while these models excel in numerical reasoning tasks, they struggle with tasks like financial named entity recognition (NER) and sentiment analysis that require domain-specific knowledge. The study also compares the strengths and limitations of ChatGPT and GPT-4 with state-of-the-art finetuned models as well as pretrained domain-specific generative models. Through qualitative experiments, the authors aim to provide insights into the existing models' capabilities and contribute to further improvements in this field. Overall, this research sheds light on the applicability of large language models like ChatGPT and GPT-4 in solving financial text analytic problems by identifying their strengths and weaknesses. It paves the way for future advancements in leveraging these models for more accurate and effective financial analysis.