Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models
Xiaojun Wu, Junxi Liu, Huanyi Su, Zhouchi Lin, Yiyan Qi, Chengjin Xu, Jiajun Su, Jiajie Zhong, Fuwei Wang, Saizhuo Wang, Fengrui Hua, Jia Li, Jian Guo

TL;DR
Golden Touchstone is a new bilingual benchmark for evaluating financial large language models across multiple tasks in Chinese and English, providing a standardized and comprehensive assessment tool for the financial AI community.
Contribution
It introduces Golden Touchstone, a comprehensive bilingual benchmark for financial LLMs, and presents Touchstone-GPT, a new financial LLM trained specifically for this benchmark.
Findings
Major models show varied strengths and weaknesses in financial tasks.
Touchstone-GPT performs well on the benchmark but has limitations in certain areas.
The benchmark reveals specific challenges in bilingual financial NLP evaluation.
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…
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Taxonomy
TopicsStock Market Forecasting Methods
