FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI

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 half-day workshop explores potential approaches and research directions to address these challenges by equipping generative models with advanced Information Retrieval (IR) models. Specifically, this 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.

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Publication Details

Published
2025-07-13
DOI
https://doi.org/10.1145/3726302.3730366
Citations
1
Primary Topic
Stock Market Forecasting Methods
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article
Field-Weighted Citation Impact
2.11
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FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI

Guangnan Ye, Fengbin Zhu, Fuli Feng, Dhagash Mehta et al.
1 citations
Stock Market Forecasting Methods
2.11
article

FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI

Guangnan Ye, Fengbin Zhu, Fuli Feng, Dhagash Mehta, Yunshan Ma, Tat‐Seng Chua, Huanbo Luan, Pingping Chen, Chao Wang, Shuo Zhang, Bing Xiang
article en
1 citations

Abstract

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 half-day workshop explores potential approaches and research directions to address these challenges by equipping generative models with advanced Information Retrieval (IR) models. Specifically, this 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.

University of Science and Technology of China (CN), National University of Singapore (SG), Fudan University (CN), Goldman Sachs (United States) (US), BlackRock (United States) (US), Singapore Management University (SG)
Openalex Percentile: Top 12%
Stock Market Forecasting Methods
2.11
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