Scope and Objective

In 2023, the FinLLM2023 workshop held in Macau achieved remarkable success, gathering a diverse group of researchers, practitioners, and industry experts to explore the intersection of pretrained large - language models (LLMs) and the financial services industry. Pretrained LLMs have since then continued to demonstrate their vast potential across numerous natural language processing tasks, including language generation, machine translation, and question-answering.In the financial services domain, the influence of these models is becoming increasingly profound. They are making a significant impact on tasks such as financial forecasting, risk management, and sentiment analysis. Moreover, LLMs are automating the arduous process of analyzing financial reports, extracting crucial insights that enable businesses to make well-informed decisions. Building on the success of FinLLM2023, this upcoming workshop FinLLM2025 aims to once again provide a global platform for all stakeholders in the field. It will facilitate the sharing of novel ideas, the exchange of cutting - edge research findings, and in - depth discussions on the challenges and opportunities that lie ahead.

To support authors who may experience difficulties obtaining Canadian visas, FinLLM2025 is going to be held in Guangzhou, China with the IJCAI satellite meeting, between August 28 and August 31, 2025. 

The workshop will cover two themes:

• Theme 1: Expanding Potential Applications and Best PracticesBuilding on the foundation laid in 2023, we will further explore the untapped potential applications of pretrained LLMs in financial services. This includes delving into new use - cases, refining existing best practices, and exploring how LLMs can be integrated more seamlessly into financial workflows.

• Theme 2: Overcoming Challenges for Efficiency, Effectiveness, and TrustThe workshop will also continue to address the critical challenges that need to be overcome to ensure the efficient, effective, and trustworthy use of LLMs in finance. This may include issues such as data privacy, model interpretability, and the mitigation of biases in financial applications.

Submission Guidelines:

We welcome submissions of both short papers (4 - 6 pages) and long papers on recent advances and applications of large language models (LLMs) in financial services. Each submitted paper will undergo a rigorous peer-review process by at least two reviewers to ensure high quality. Your Paper should be submitted in easychair.org website or to our co-organized special issue in FITEE(https://www.fitee.zjujournals.com/en/news/184388/) following the guideline https://www.fitee.zjujournals.com/en/news/184388/.

It is recommended to use  the template available at https://www.fitee.zjujournals.com/en/about/7144/.

Papers will be considered for publication in the Frontiers of Information Technology & Electronic Engineering (FITEE) special issue on "Theory and Applications of Financial Large Models"( https://www.fitee.zjujournals.com/en/news/184388/). FITEE is a peer - reviewed journal jointly launched by the Chinese Academy of Engineering (CAE) and Zhejiang University, co - published by Springer & Zhejiang University Press, and indexed in SCI - E with an Impact Factor of 2.7 (2023 JCR). It is important to note that papers recommended for FITEE will undergo a separate and independent peer - review process, distinct from the workshop's review process. This means that acceptance is not guaranteed, as it will be determined by each journal's specific evaluation criteria, available publication space, and the overall fit of the research with the journal's scope.

Additionally, 1–3 outstanding authors will be selected to receive sponsorship for their IJCAI 2025 conference registration fees, with student authors given priority under equal circumstances.

We encourage submissions that cover a wide range of topics, including but not limited to:

Techniques:

-Multimodal modeling of financial data using LLMs-Preprocessing and cleaning of financial data for use with LLMs-Integration of LLMs with other AI technologies in financial services-Novel architectures and training techniques for LLMs in financial services.

-Scalability and efficiency of LLMs in financial services-Cross-lingual and multilingual LLMs in financial services-Human-in-the-loop approaches for LLMs in financial services

Applications:

-Financial forecasting using LLMs-Sentiment analysis and opinion mining for financial data-LLM-based trading algorithms and decision-making systems-Analysis of financial news and social media using LLMs-Semantic analysis of financial reports and filings

-Explainable AI in financial services using LLMs-Transfer learning and domain adaptation for LLMs in financial services-Case studies and success stories of LLMs in financial services

Challenges:

-Evaluation of LLMs for financial services-Social economics and trustworthiness for LLMs in financial services-Ethical and legal considerations in the use of LLMs in financial services

-Privacy and security concerns in the use of LLMs for financial data-Bias and fairness considerations in the use of LLMs for financial services

Contact

All questions about submissions should be emailed to yanjiangpeng@efunds.com.cn