@inproceedings{guo-etal-2023-chatgpt,
title = "Is {C}hat{GPT} a Financial Expert? Evaluating Language Models on Financial Natural Language Processing",
author = "Guo, Yue and
Xu, Zian and
Yang, Yi",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.58/",
doi = "10.18653/v1/2023.findings-emnlp.58",
pages = "815--821",
abstract = "The emergence of Large Language Models (LLMs), such as ChatGPT, has revolutionized general natural language preprocessing (NLP) tasks. However, their expertise in the financial domain lacks a comprehensive evaluation. To assess the ability of LLMs to solve financial NLP tasks, we present FinLMEval, a framework for Financial Language Model Evaluation, comprising nine datasets designed to evaluate the performance of language models. This study compares the performance of fine-tuned auto-encoding language models (BERT, RoBERTa, FinBERT) and the LLM ChatGPT. Our findings reveal that while ChatGPT demonstrates notable performance across most financial tasks, it generally lags behind the fine-tuned expert models, especially when dealing with proprietary datasets. We hope this study builds foundation evaluation benchmarks for continuing efforts to build more advanced LLMs in the financial domain."
}
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<abstract>The emergence of Large Language Models (LLMs), such as ChatGPT, has revolutionized general natural language preprocessing (NLP) tasks. However, their expertise in the financial domain lacks a comprehensive evaluation. To assess the ability of LLMs to solve financial NLP tasks, we present FinLMEval, a framework for Financial Language Model Evaluation, comprising nine datasets designed to evaluate the performance of language models. This study compares the performance of fine-tuned auto-encoding language models (BERT, RoBERTa, FinBERT) and the LLM ChatGPT. Our findings reveal that while ChatGPT demonstrates notable performance across most financial tasks, it generally lags behind the fine-tuned expert models, especially when dealing with proprietary datasets. We hope this study builds foundation evaluation benchmarks for continuing efforts to build more advanced LLMs in the financial domain.</abstract>
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%0 Conference Proceedings
%T Is ChatGPT a Financial Expert? Evaluating Language Models on Financial Natural Language Processing
%A Guo, Yue
%A Xu, Zian
%A Yang, Yi
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F guo-etal-2023-chatgpt
%X The emergence of Large Language Models (LLMs), such as ChatGPT, has revolutionized general natural language preprocessing (NLP) tasks. However, their expertise in the financial domain lacks a comprehensive evaluation. To assess the ability of LLMs to solve financial NLP tasks, we present FinLMEval, a framework for Financial Language Model Evaluation, comprising nine datasets designed to evaluate the performance of language models. This study compares the performance of fine-tuned auto-encoding language models (BERT, RoBERTa, FinBERT) and the LLM ChatGPT. Our findings reveal that while ChatGPT demonstrates notable performance across most financial tasks, it generally lags behind the fine-tuned expert models, especially when dealing with proprietary datasets. We hope this study builds foundation evaluation benchmarks for continuing efforts to build more advanced LLMs in the financial domain.
%R 10.18653/v1/2023.findings-emnlp.58
%U https://aclanthology.org/2023.findings-emnlp.58/
%U https://doi.org/10.18653/v1/2023.findings-emnlp.58
%P 815-821
Markdown (Informal)
[Is ChatGPT a Financial Expert? Evaluating Language Models on Financial Natural Language Processing](https://aclanthology.org/2023.findings-emnlp.58/) (Guo et al., Findings 2023)
ACL