黃金試金石：用於評估金融大型語言模型的全面雙語基準

Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models

November 9, 2024

作者: 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

cs.AI

摘要

隨著大型語言模型在金融領域中變得日益普遍，迫切需要一種標準化方法來全面評估其性能。然而，現有的金融基準測試往往存在語言和任務範圍有限，以及低質量數據集和不足適應性用於大型語言模型評估等挑戰。為了解決這些限制，我們提出了「金色基準」，這是第一個針對金融語言模型的全面雙語基準測試，涵蓋了來自中文和英文的代表性數據集，涵蓋了八個核心金融自然語言處理任務。通過廣泛的開源數據收集和行業特定需求的開發，這個基準測試包括各種金融任務，旨在全面評估模型的語言理解和生成能力。通過在基準測試上對主要模型進行比較分析，如GPT-4o Llama3、FinGPT和FinMA，我們揭示了它們在處理複雜金融信息方面的優勢和限制。此外，我們開源了Touchstone-GPT，這是通過持續預訓練和金融指導調整訓練的金融語言模型，在雙語基準測試中表現出色，但在特定任務上仍存在限制。這項研究不僅為金融大型語言模型提供了一個實用的評估工具，還指導了未來研究的發展和優化。Golden Touchstone的源代碼和Touchstone-GPT的模型權重已經公開在https://github.com/IDEA-FinAI/Golden-Touchstone，有助於金融語言模型的持續演進，並促進這一關鍵領域的進一步研究。

English

As large language models become increasingly prevalent in the financial sector, there is a pressing need for a standardized method to comprehensively assess their performance. However, existing finance benchmarks often suffer from limited language and task coverage, as well as challenges such as low-quality datasets and inadequate adaptability for LLM evaluation. To address these limitations, we propose "Golden Touchstone", the first comprehensive bilingual benchmark for financial LLMs, which incorporates representative datasets from both Chinese and English across eight core financial NLP tasks. Developed from extensive open source data collection and industry-specific demands, this benchmark includes a variety of financial tasks aimed at thoroughly assessing models' language understanding and generation capabilities. Through comparative analysis of major models on the benchmark, such as GPT-4o Llama3, FinGPT and FinMA, we reveal their strengths and limitations in processing complex financial information. Additionally, we open-sourced Touchstone-GPT, a financial LLM trained through continual pre-training and financial instruction tuning, which demonstrates strong performance on the bilingual benchmark but still has limitations in specific tasks.This research not only provides the financial large language models with a practical evaluation tool but also guides the development and optimization of future research. 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, contributing to the ongoing evolution of FinLLMs and fostering further research in this critical area.