@inproceedings{wu-etal-2025-golden,
title = "Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models",
author = "Wu, Xiaojun and
Liu, Junxi and
Su, Huan-Yi and
Lin, Zhouchi and
Qi, Yiyan and
Xu, Chengjin and
Su, Jiajun and
Zhong, Jiajie and
Wang, Fuwei and
Wang, Saizhuo and
Hua, Fengrui and
Li, Jia and
Guo, Jian",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1227/",
doi = "10.18653/v1/2025.findings-emnlp.1227",
pages = "22544--22560",
ISBN = "979-8-89176-335-7",
abstract = "As large language models (LLMs) increasingly permeate the financial sector, there is a pressing need for a standardized method to comprehensively assess their performance. Existing financial benchmarks often suffer from limited language and task coverage, low-quality datasets, and inadequate adaptability for LLM evaluation. To address these limitations, we introduce Golden Touchstone, a comprehensive bilingual benchmark for financial LLMs, encompassing eight core financial NLP tasks in both Chinese and English. Developed from extensive open-source data collection and industry-specific demands, this benchmark thoroughly assesses models' language understanding and generation capabilities. Through comparative analysis of major models such as GPT-4o, Llama3, FinGPT, and FinMA, we reveal their strengths and limitations in processing complex financial information. Additionally, we open-source Touchstone-GPT, a financial LLM trained through continual pre-training and instruction tuning, which demonstrates strong performance on the bilingual benchmark but still has limitations in specific tasks. This research provides a practical evaluation tool for financial LLMs and guides future development and optimization.The source code for Golden Touchstone and model weight of Touchstone-GPT have been made publicly available at \url{https://github.com/IDEA-FinAI/Golden-Touchstone}."
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<abstract>As large language models (LLMs) increasingly permeate the financial sector, there is a pressing need for a standardized method to comprehensively assess their performance. Existing financial benchmarks often suffer from limited language and task coverage, low-quality datasets, and inadequate adaptability for LLM evaluation. To address these limitations, we introduce Golden Touchstone, a comprehensive bilingual benchmark for financial LLMs, encompassing eight core financial NLP tasks in both Chinese and English. Developed from extensive open-source data collection and industry-specific demands, this benchmark thoroughly assesses models’ language understanding and generation capabilities. Through comparative analysis of major models such as GPT-4o, Llama3, FinGPT, and FinMA, we reveal their strengths and limitations in processing complex financial information. Additionally, we open-source Touchstone-GPT, a financial LLM trained through continual pre-training and instruction tuning, which demonstrates strong performance on the bilingual benchmark but still has limitations in specific tasks. This research provides a practical evaluation tool for financial LLMs and guides future development and optimization.The source code for Golden Touchstone and model weight of Touchstone-GPT have been made publicly available at https://github.com/IDEA-FinAI/Golden-Touchstone.</abstract>
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%0 Conference Proceedings
%T Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models
%A Wu, Xiaojun
%A Liu, Junxi
%A Su, Huan-Yi
%A Lin, Zhouchi
%A Qi, Yiyan
%A Xu, Chengjin
%A Su, Jiajun
%A Zhong, Jiajie
%A Wang, Fuwei
%A Wang, Saizhuo
%A Hua, Fengrui
%A Li, Jia
%A Guo, Jian
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F wu-etal-2025-golden
%X As large language models (LLMs) increasingly permeate the financial sector, there is a pressing need for a standardized method to comprehensively assess their performance. Existing financial benchmarks often suffer from limited language and task coverage, low-quality datasets, and inadequate adaptability for LLM evaluation. To address these limitations, we introduce Golden Touchstone, a comprehensive bilingual benchmark for financial LLMs, encompassing eight core financial NLP tasks in both Chinese and English. Developed from extensive open-source data collection and industry-specific demands, this benchmark thoroughly assesses models’ language understanding and generation capabilities. Through comparative analysis of major models such as GPT-4o, Llama3, FinGPT, and FinMA, we reveal their strengths and limitations in processing complex financial information. Additionally, we open-source Touchstone-GPT, a financial LLM trained through continual pre-training and instruction tuning, which demonstrates strong performance on the bilingual benchmark but still has limitations in specific tasks. This research provides a practical evaluation tool for financial LLMs and guides future development and optimization.The source code for Golden Touchstone and model weight of Touchstone-GPT have been made publicly available at https://github.com/IDEA-FinAI/Golden-Touchstone.
%R 10.18653/v1/2025.findings-emnlp.1227
%U https://aclanthology.org/2025.findings-emnlp.1227/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1227
%P 22544-22560
Markdown (Informal)
[Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models](https://aclanthology.org/2025.findings-emnlp.1227/) (Wu et al., Findings 2025)
ACL
- Xiaojun Wu, Junxi Liu, Huan-Yi Su, Zhouchi Lin, Yiyan Qi, Chengjin Xu, Jiajun Su, Jiajie Zhong, Fuwei Wang, Saizhuo Wang, Fengrui Hua, Jia Li, and Jian Guo. 2025. Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 22544–22560, Suzhou, China. Association for Computational Linguistics.