FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets
University of California, Los Angeles（加州大学洛杉矶分校）；NYU Shanghai（上海纽约大学）；Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning（上海人工智能与深度学习前沿科学中心）；Columbia University（哥伦比亚大学）
Neng Wang, Hongyang Yang, Christina Dan Wang
备注：
Workshop on Instruction Tuning and Instruction Following at NeurIPS 2023
In the swiftly expanding domain of Natural Language Processing (NLP), the potential of GPT-based models for the financial sector is increasingly evident. However, the integration of these models with financial datasets presents challenges, notably in determining their adeptness and relevance. This paper introduces a distinctive approach anchored in the Instruction Tuning paradigm for open-source large language models, specifically adapted for financial contexts. Through this methodology, we capitalize on the interoperability of open-source models, ensuring a seamless and transparent integration. We begin by explaining the Instruction Tuning paradigm, highlighting its effectiveness for immediate integration. The paper presents a benchmarking scheme designed for end-to-end training and testing, employing a cost-effective progression. Firstly, we assess basic competencies and fundamental tasks, such as Named Entity Recognition (NER) and sentiment analysis to enhance specialization. Next, we delve into a comprehensive model, executing multi-task operations by amalgamating all instructional tunings to examine versatility. Finally, we explore the zero-shot capabilities by earmarking unseen tasks and incorporating novel datasets to understand adaptability in uncharted terrains. Such a paradigm fortifies the principles of openness and reproducibility, laying a robust foundation for future investigations in open-source financial large language models (FinLLMs).
Faculty of Information Science；Technology Hokkaido University；School of Engineering The University of Tokyo 0000-0001-8519-5617；Technology Hokkaido University 0000-0001-5030-625X；Technology Hokkaido University 0000-0003-1987-5336
Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models
IDEA Research（IDEA研究机构）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；The Hong Kong University of Science and Technology（香港科学与技术大学）；Nanjing University（南京大学）；South China Normal University（华南师范大学）；DataArcTech Ltd.（DataArcTech有限公司）
No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks
Yunnan University（云南大学）；The University of Manchester（曼彻斯特大学）；Wuhan University（武汉大学）；University of Florida（佛罗里达大学）；The Chinese University of Hong Kong（香港中文大学）；The Fin AI
Financial Services Innovation Lab, Georgia Institute of Technology（佐治亚理工学院金融服务创新实验室）；College of Business, Georgia Institute of Technology（佐治亚理工学院商学院）；College of Computing, Georgia Institute of Technology（佐治亚理工学院计算学院）
Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English
The Fin AI；University of Manchester（曼彻斯特大学）；School of Computer Science Sichuan University（四川大学计算机学院）；School of Computer Science Wuhan University（武汉大学计算机学院）；University of Florida（佛罗里达大学）；Columbia University（哥伦比亚大学）
针对西班牙语金融NLP研究不足的问题，提出首个双语框架Toisón de Oro，构建指令数据集与评估基准，并推出FinMA-ES模型，在西班牙语金融任务中超越GPT-4等SOTA模型。
Exploring Large Language Models for Financial Applications: Techniques, Performance, and Challenges with FinMA
Lyman Briggs College, Michigan State University（密歇根州立大学Lyman Briggs学院）；Department of Finance, Michigan State University（密歇根州立大学金融系）；African Institute for Mathematical Sciences, Rwanda（刚果(金)数学科学研究所）
Revolutionizing Finance with LLMs: An Overview of Applications and Insights
School of Computing, The University of Georgia（佐治亚大学计算机学院）；Computer Science and Engineering, The University of Texas at Arlington（德克萨斯大学阿灵顿分校计算机科学与工程系）；Department of Geography, University of Georgia（佐治亚大学地理系）
Beyond Classification: Financial Reasoning in State-of-the-Art Language Models
Seoul National University of Science and Technology（首尔国立科技大学）；Seoul National University（首尔国立大学）；MODULABS；Yonsei University（延世大学）；OneLineAI；Chung-Ang University（中央大学）
Instruction Learning Paradigms: A Dual Perspective on White-box and Black-box LLMs
School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；Nanyang Technological University（南洋理工大学）；University of Leicester（莱斯特大学）；Kuaishou Technology（快手科技）
Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models
Department of Informatics, University of Sussex（信息学院，苏塞克斯大学）；Pingtan Research Institute, Xiamen University（平潭研究院，厦门大学）；Department of Computer Science, University of Liverpool（计算机科学系，利物浦大学）；Department of Computer Science, Purdue University（计算机科学系，普渡大学）；Department of Computer Science, Emory University（计算机科学系，埃默里大学）；AI Agent Lab, Vokram Group（AI代理实验室，Vokram集团）；Department of Computing, Imperial College London（计算系，帝国理工学院伦敦分校）
Cost-effective Instruction Learning for Pathology Vision and Language Analysis
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Computer Science, Fudan University（复旦大学计算机学院）；National Biomedical Imaging Center, College of Future Technology, Peking University（北京大学未来技术学院生物医学成像中心）；Ruijin Hospital, Shanghai Jiaotong University School of Medicine（上海交通大学医学院瑞金医院）；Department of Pathology, State Key Laboratory of Cancer Biology, Xijing Hospital（西京医院病理科，癌症生物学国家重点实验室）；Department of Computer Science, Rutgers University（罗格斯大学计算机科学系）
FinSphere, a Real-Time Stock Analysis Agent Powered by Instruction-Tuned LLMs and Domain Tools
JF SmartInvest Holdings Ltd.（JF SmartInvest控股有限公司）；Columbia University（哥伦比亚大学）；Shanghai University of Finance and Economics（上海财经大学）；Johns Hopkins University（约翰霍普金斯大学）
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
The Fin AI（Fin AI）；Wuhan University（武汉大学）；Columbia University（哥伦比亚大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Nanjing University（南京大学）；Rensselaer Polytechnic Institute（罗格斯理工学院）；The University of Manchester（曼彻斯特大学）；Stevens Institute of Technology（史泰斯理工学院）；National University of Singapore（新加坡国立大学）；University of Florida（佛罗里达大学）；University of Montreal（蒙特利尔大学）；Yale University（耶鲁大学）；New York University（纽约大学）；Harvard University（哈佛大学）；NVIDIA（英伟达）；Artificial Intelligence Research Centre（人工智能研究中心）；Archimedes/Athena Research Centre（Archimedes/Athena研究中心）
Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach
Columbia University（哥伦比亚大学）；Central University of Finance and Economics（中央财经大学）；New York University（纽约大学）；Cornell University（康奈尔大学）；University of Michigan（密歇根大学）；Rutgers University（罗格斯大学）
Business as Rulesual: A Benchmark and Framework for Business Rule Flow Modeling with LLMs
Zhejiang University of Technology（浙江工业大学）；Zhejiang Key Laboratory of Visual Information Intelligent Processing（浙江省视觉信息智能处理重点实验室）；University of Aberdeen（阿伯丁大学）；University of Birmingham（伯明翰大学）
