@inproceedings{nie-etal-2025-cfinbench,
title = "{CF}in{B}ench: A Comprehensive {C}hinese Financial Benchmark for Large Language Models",
author = "Nie, Ying and
Yan, Binwei and
Guo, Tianyu and
Liu, Hao and
Wang, Haoyu and
He, Wei and
Zheng, Binfan and
Wang, Weihao and
Li, Qiang and
Sun, Weijian and
Wang, Yunhe and
Tao, Dacheng",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.40/",
doi = "10.18653/v1/2025.naacl-long.40",
pages = "876--891",
ISBN = "979-8-89176-189-6",
abstract = "Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging task like finance, has not been fully explored. In this paper, we present CFinBench: a meticulously crafted, the most comprehensive evaluation benchmark to date, for assessing the financial knowledge of LLMs under Chinese context. In practice, to better align with the career trajectory of Chinese financial practitioners, we build a systematic evaluation from 4 first-level categories: (1) Financial Subject: whether LLMs can memorize the necessary basic knowledge of financial subjects, such as economics, statistics and auditing. (2) Financial Qualification: whether LLMs can obtain the needed financial qualified certifications, such as certified public accountant, securities qualification and banking qualification. (3) Financial Practice: whether LLMs can fulfill the practical financial jobs, such as tax consultant, junior accountant and securities analyst. (4) Financial Law: whether LLMs can meet the requirement of financial laws and regulations, such as tax law, insurance law and economic law. CFinBench comprises 99,100 questions spanning 43 second-level categories with 3 question types: single-choice, multiple-choice and judgment. We conduct extensive experiments on a wide spectrum of representative LLMs with various model size on CFinBench. The results show that GPT4 and some Chinese-oriented models lead the benchmark, with the highest average accuracy being 66.02{\%}, highlighting the challenge presented by CFinBench. All the data and evaluation code are open sourced at https://cfinbench.github.io/"
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<abstract>Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging task like finance, has not been fully explored. In this paper, we present CFinBench: a meticulously crafted, the most comprehensive evaluation benchmark to date, for assessing the financial knowledge of LLMs under Chinese context. In practice, to better align with the career trajectory of Chinese financial practitioners, we build a systematic evaluation from 4 first-level categories: (1) Financial Subject: whether LLMs can memorize the necessary basic knowledge of financial subjects, such as economics, statistics and auditing. (2) Financial Qualification: whether LLMs can obtain the needed financial qualified certifications, such as certified public accountant, securities qualification and banking qualification. (3) Financial Practice: whether LLMs can fulfill the practical financial jobs, such as tax consultant, junior accountant and securities analyst. (4) Financial Law: whether LLMs can meet the requirement of financial laws and regulations, such as tax law, insurance law and economic law. CFinBench comprises 99,100 questions spanning 43 second-level categories with 3 question types: single-choice, multiple-choice and judgment. We conduct extensive experiments on a wide spectrum of representative LLMs with various model size on CFinBench. The results show that GPT4 and some Chinese-oriented models lead the benchmark, with the highest average accuracy being 66.02%, highlighting the challenge presented by CFinBench. All the data and evaluation code are open sourced at https://cfinbench.github.io/</abstract>
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%0 Conference Proceedings
%T CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models
%A Nie, Ying
%A Yan, Binwei
%A Guo, Tianyu
%A Liu, Hao
%A Wang, Haoyu
%A He, Wei
%A Zheng, Binfan
%A Wang, Weihao
%A Li, Qiang
%A Sun, Weijian
%A Wang, Yunhe
%A Tao, Dacheng
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F nie-etal-2025-cfinbench
%X Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging task like finance, has not been fully explored. In this paper, we present CFinBench: a meticulously crafted, the most comprehensive evaluation benchmark to date, for assessing the financial knowledge of LLMs under Chinese context. In practice, to better align with the career trajectory of Chinese financial practitioners, we build a systematic evaluation from 4 first-level categories: (1) Financial Subject: whether LLMs can memorize the necessary basic knowledge of financial subjects, such as economics, statistics and auditing. (2) Financial Qualification: whether LLMs can obtain the needed financial qualified certifications, such as certified public accountant, securities qualification and banking qualification. (3) Financial Practice: whether LLMs can fulfill the practical financial jobs, such as tax consultant, junior accountant and securities analyst. (4) Financial Law: whether LLMs can meet the requirement of financial laws and regulations, such as tax law, insurance law and economic law. CFinBench comprises 99,100 questions spanning 43 second-level categories with 3 question types: single-choice, multiple-choice and judgment. We conduct extensive experiments on a wide spectrum of representative LLMs with various model size on CFinBench. The results show that GPT4 and some Chinese-oriented models lead the benchmark, with the highest average accuracy being 66.02%, highlighting the challenge presented by CFinBench. All the data and evaluation code are open sourced at https://cfinbench.github.io/
%R 10.18653/v1/2025.naacl-long.40
%U https://aclanthology.org/2025.naacl-long.40/
%U https://doi.org/10.18653/v1/2025.naacl-long.40
%P 876-891
Markdown (Informal)
[CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models](https://aclanthology.org/2025.naacl-long.40/) (Nie et al., NAACL 2025)
ACL
- Ying Nie, Binwei Yan, Tianyu Guo, Hao Liu, Haoyu Wang, Wei He, Binfan Zheng, Weihao Wang, Qiang Li, Weijian Sun, Yunhe Wang, and Dacheng Tao. 2025. CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 876–891, Albuquerque, New Mexico. Association for Computational Linguistics.