Recent advancements in Generative AI, such as Large Language Models (LLMs), have demonstrated remarkable success across various general tasks. Extensive studies have explored leveraging generative models in finance, but significant challenges persist. This FinIR workshop aims to explore potential approaches and research directions to address these challenges by equipping generative models with advanced Information Retrieval (IR) models. Specifically, this FinIR workshop seeks to provide a platform for discussing innovative ideas that facilitate the advancement of IR technology to enrich generative models in finance from four key perspectives: (i) financial IR techniques, (ii) financial IR benchmarking and evaluation, (iii) financial systems and agents/assistants, (iv) and trustworthiness, privacy and security when applying financial IR and generative models. This workshop aims to deepen understanding, accelerate progress, and support the advancement of IR technology to enhance generative models to address financial challenges.

We welcome contributions of research papers from a wide range of topics, including but not limited to the following topics of interest:

• Financial IR Techniques
  • Financial retrieval models including table retrieval, multi-modal retrieval, real-time retrieval, and generative retrieval, etc.
  • Techniques for financial query analysis, query rephrase, query expansion, etc.
  • Techniques for financial data processing, de-duplication, representation, indexing, filtering, and ranking, etc.
• Financial IR Evaluation
  • Financial IR tasks and benchmarks for evaluating IR models in finance.
  • Evaluation metrics and methodologies for measuring financial IR models.
  • Evaluation with simulators, such as market simulator.
• Financial Systems and Agents
  • Utilizing IR and generative models to develop systems and applications for financial data analysis, stock price prediction, stock recommendation, portfolio optimization, financial content generation, event forecasting, etc.
  • Financial conversational agents and personal financial assistants powered by IR and generative models.
  • Multi-agent systems in finance for algorithmic trading, policy design, behavior modeling, risk assessment, etc.
• Trustworthiness, Privacy and Security
  • Exploring and resolving the challenges related to trustworthiness and reliability in applying IR and generative models in finance, including distinguishing AI-generated financial content and ensuring factual consistency, etc.
  • Privacy-preserving access and processing financial data, such as sensitive information anonymization and privacy regulations compliance (e.g., PDPA and GDPR).
  • Financial data security when using IR technology and generative models, such as financial misinformation detection and data leakage prevention.

PAPER SUBMISSION GUIDLINES

Submissions may range from 4 to 8 pages, with no limit on the number of pages for references. Authors can choose the appropriate length for their paper, as no distinction is made between long and short submissions. All submissions are double-blind and will be peer reviewed by the program committee and judged by their relevance to the workshop, especially to the topics, and their potential to generate discussion.

All submissions should be prepared according to the standard double-column ACM SIG proceedings format. Additional information about formatting and style files is available on the ACM website. Papers must be submitted to easychair at Submission site.

For inquires about the workshop and submissions, please email to finir2025@easychair.org