The FinNLP workshop, as the annual event of ACL SIG-FinTech, focuses on applying NLP, Machine Learning, and Large Language Models to finance, economics, and law, aiming to drive interdisciplinary innovation and address unique challenges in these fields. Since 2019, FinNLP has served as a bridge between AI and NLP communities by collocating with major conferences. Its proceedings are available on the ACL Anthology.

FinNLP addresses the complexities of financial documents, which often contain tables and diverse multimodal data, as well as legal and compliance challenges in the financial industry. Key workshop topics include financial language modeling, multi-modal and graph representation learning, conversational agents, financial search and question answering, event discovery, compliance, and responsible AI practices in finance and law.

We have several interesting shared tasks in the collocated event, FinEval. Please refer to the FinEval page for more details.

We invite submissions of original contributions on methods, theories, applications, and systems on artificial intelligence, machine learning, natural language processing & understanding, big data, statistical learning, data analytics, and deep learning, with a focus on knowledge discovery in the financial services domain. The scope of the workshop includes, but is not limited to, the following areas:

• Language modeling on financial corpora, including tabular and numerical data, and multi-modal modeling
• Graph representation learning and mining on financial data
• Multi-source knowledge integration and fusion
• Synthetic and genuine financial datasets and benchmarks
• Transfer learning applications for financial data
• Financial search and question answering systems
• Event discovery and impact on organizational equity price
• ESG event discovery, evaluation, and impact assessment
• Compliance monitoring
• Cross-disciplinary LLM-based methodologies for financial and legal domains
• Applications of LLMs in financial auditing and regulatory reporting
• Ethical implications and bias mitigation in AI applications
• Hallucination mitigation and evaluation in LLMs
• Privacy concerns and data protection strategies
• Enhancing interpretability and explainability of LLM models
• Responsible AI practices and governance
• Methods, evaluation metrics, benchmarks, and datasets for LLMs in finance and law

• Submission Deadline: TBA
• Submission System: TBA
• Paper Notification: TBA
• Camera-Ready Deadline: TBA
• FinNLP-2025@EMNLP: TBA
• Time zone: Anywhere On Earth (AOE)

The ACL Template MUST be used for your submission(s). Accepted papers proceedings will be published at ACL Anthology.

• Long Paper: May consist of up to 8 pages of content, plus unlimited pages for references and appendix.
• Short Paper and Demo Paper: May consist of up to 4 pages of content, plus unlimited references and appendix.

• The reviewing process will be double-blind for Long and Short Paper, and single-blind for Demo Paper. Submissions must be in electronic form using the paper submission link above.
• At least one author of each accepted paper should register and present their work in FinNLP.

• Chung-Chi Chen - AIST, Japan
• Genta Indra Winata - Capital One, US
• Stephen Rawls - Capital One, US
• Anirban Das - Capital One, US
• Hsin-Hsi Chen - National Taiwan University, Taiwan
• Hiroya Takamura - AIST, Japan

• Sophia Ananiadou - University of Manchester, UK
• Hen-Hsen Huang - Academia Sinica, Taiwan
• Shi-Xiong Zhang - Capital One, US
• Supriyo Chakraborty - Capital One, US
• Sambit Sahu - Capital One, US

TBA