The 1st Workshop on LLMs and Generative AI in Finance

The 1st Workshop on

LLMs and Generative AI for Finance

The 1st Workshop on LLMs and Generative AI
for Finance

Workshop at ICAIF '24

📅 Key Dates:

Workshop: November 14th (Thu), 2024, 13:00 - 17:30 EST

📍 Location:
The Tillary Hotel Brooklyn, LL1 (Lower Level 1)
— 85 Flatbush Ave Ext, Brooklyn, NY 11201, United States

This event is limited to in-person attendance, thank you.

Contact: contact@ai4f.org

If you haven't registered yet, please sign up first.
If you are already registered, please click check-in and fill out the Google Form.

For those who have been accepted as poster presenters, the recommended sizes are as follows (landscape):

40 inches (width) x 30 inches (height) (101 cm x 76 cm)

📅 Key Dates:

Workshop: November 14th (Thu), 2024, 13:00 - 17:30 EST

📍 Location:
The Tillary Hotel Brooklyn, LL1 (Lower Level 1)
— 85 Flatbush Ave Ext, Brooklyn, NY 11201, United States

This event is limited to in-person attendance, thank you.

Contact: contact@ai4f.org

If you haven't registered yet, please sign up first.
If you are already registered, please click check-in and fill out the Google Form.

Registration

Poster Template

Poster
Template

Check-in

Speakers

Keynote

Prof. Valeri Nikolaev

University of Chicago Booth School
of Business

Keynote

Prof. Andrew Lo

MIT Sloan School of Management

Keynote

Prof. Ralph Koijen

University of Chicago Booth School
of Business

Opening Remarks

Prof. Yoon Kim

MIT Electrical Engineering and
Computer Science (EECS)

Short talk

Prof. Sean Cao

University of Maryland,
Robert H. Smith School of Business

Short talk

Prof. Miao Liu

Boston College, Carroll School of Management

Short talk

Armineh Nourbakhsh

J.P. Morgan AI Research Executive Director

Keynote

Prof. Valeri Nikolaev

University of Chicago,
Booth School of Business

Keynote

Prof. Andrew Lo

MIT, Sloan School of Management

Keynote

Prof. Ralph Koijen

University of Chicago,
Booth School of Business

Opening Remarks

Prof. Yoon Kim

MIT, Electrical Engineering and
Computer Science

Short talk

Prof. Sean Cao

University of Maryland,
Robert H. Smith School of Business

Short talk

Prof. Miao Liu

Boston College, Carroll School of Management

Short talk

Armineh Nourbakhsh

J.P. Morgan AI Research

Executive Director

Panelists

Naftali Cohen

Schonfeld

Panelist

Andrew Chin

AllianceBernstein

Panelist

Yu Yu

BlackRock

Panelist

Ben Wellington

Two Sigma

Panelist

Edward Tong

Millennium

Panelist

Hojun Choi

Linq

Moderator

Organizing Committee

Prof. Yoon Kim

MIT, Electrical Engineering
and Computer Science

Prof. Valeri Nikolaev

University of Chicago, Booth School of Business

Prof. Andrew Lo

MIT, Sloan School of Management

Dr. Jacob
Chanyeol Choi

Linq

Prof. Alejandro
Lopez Lira

University of Florida, Warrington College of Business

Prof. Eric So

MIT, Sloan School of Management

Dr. Yu Yu

BlackRock

Prof. Jy-yong Sohn

Yonsei University,

Department of Applied Statistics

Prof. Elizabeth
Blankespoor

University of Washington,

Foster School of Business

Alex Kim

PhD Candidate,

University of Chicago

Booth School of Business

David Kim

PhD Candidate,

MIT Sloan School of Management

Sangwon Yoon, Esq

Seoul National University

Dr. Georgios
Papaioannou

Qube Research & Technologies

Prof. Alejandro
Lopez Lira

Warrington College of Business, University of Florida

Dr. Jacob
Chanyeol Choi

Linq

Prof. Eric So

MIT Sloan School of Management

Prof. Yoon Kim

MIT Electrical Engineering
and Computer Science
(EECS)

Prof. Valeri Nikolaev

University of Chicago Booth School of Business

Prof. Andrew Lo

MIT Sloan School of Management

Prof. Elizabeth
Blankespoor

Foster School of Business,
University of Washington

Alex Kim

PhD Candidate, University of Chicago Booth School of Business

Dr. Yu Yu

BlackRock

Prof. Jy-yong Sohn

School of Business,
Yonsei University

David Kim

PhD Candidate, MIT Sloan School of Management

Sangwon Yoon, Esq

Seoul National University

Dr. Georgios
Papaioannou

Qube Research & Technologies

The goal of the workshop is
to study how large language models (LLMs) and generative AI can be used for finance and accounting applications.

The goal of the workshop is
to bridge the gap between advancements in Large Language Models (LLMs) and Natural Language Processing (NLP) and their application in finance and accounting.
It aims to address the challenges of using these technologies in the financial domain, such as handling unstructured data, context window limitations, hallucinations, and the need for interpretability.

Panelists

Naftali Cohen

Schonfeld

Andrew Chin

AllianceBernstein

Ben Wellington

Two Sigma

Yu Yu

Blackrock

Edward Tong

Millennium

Hojun Choi

Linq

About
the workshop

The rapid advancements in large language models (LLMs) and generative AI more broadly have significantly impacted the interpretation and utilization of unstructured financial data, enabling a wide range of applications in finance and accounting. While LLMs have unlocked new possibilities in this domain, NLP techniques more broadly remain vital for various applications such as financial analysis, case studies, price forecasting, portfolio optimization, and analyzing financial reports and alternative data. They are also crucial in financial risk modeling, such as credit assessment, bankruptcy detection, and M&A target prediction using news sentiment and topic detection.

However, LLMs and NLP techniques face challenges in the financial domain, including the limited context window of LLMs, the complexities of converting unstructured data into structured formats, hallucinations, and the need for interpretability in financial applications. This workshop aims to bridge the gap between technological advancements in computer science and the specific needs of finance and accounting. By focusing on methodologies such as fine-tuning, retrieval-augmented generation (RAG), prompt engineering, and NLP, we aim to showcase how these techniques can improve the accuracy and relevance of insights derived from financial and accounting data. The ultimate goal is to foster interdisciplinary collaboration, enhance industry solutions, and contribute to academic research in finance and accounting.

The workshop will be held in conjunction with the ICAIF ’24 and is exclusively an in-person event.

Call for papers

Submit

We invite papers addressing domain-specific problems in finance and accounting, utilizing AI. While we encourage applications that involve the use of LLMs, we also invite papers that use generative AI technologies (broadly construed) to address problems in finance and accounting. Potential topics include but are not limited to:

We invite papers addressing domain-specific tasks in finance and accounting, utilizing techniques in LLMs and NLP. While the use of LLMs is recommended, submissions are not limited to LLMs and may include broader NLP methodologies. Potential topics include but are not limited to:

Methodologies for Processing Unstructured Data
Techniques to construct structured data from unstructured financial data using LLMs and NLP.
Technical Challenges of LLMs and NLP
Addressing issues such as hallucination, interpretability, and the integration of NLP methods in financial applications.
Prompt Engineering
Optimizing LLM prompts to extract relevant financial insights.

Retrieval-Augmented Generation (RAG)
Integrating external data sources with LLMs and NLP techniques for enhanced information retrieval.
Fine-Tuning LLMs
Customizing LLMs for specific financial applications to improve accuracy and relevance.
Text-to-SQL Applications
Translating natural language queries into SQL for querying structured databases using LLMs and NLP.

Evaluation Techniques
Enhancing the verifiability of generated text by LLMs and NLP methods to streamline manual verification processes.
Applications in Analyzing Financial Reports and Alternative Data
Utilizing LLMs and NLP for analyzing financial documents, news, SEC filings, and alternative data such as social media and video content.
Financial Risk Modeling
Applying LLMs and NLP in credit assessment, bankruptcy detection, and other risk modeling scenarios.

Multi-Lingual ESG Identification and Assessment
everaging LLMs and NLP for automated ESG scoring and identifying ESG issues across languages.
Financial Fraud Detection
Using NLP approaches, including those involving LLMs, for detecting fraud in financial transactions.
Enhancing Investor Communication
Utilizing LLMs and NLP to improve investor relations and communication.

Retrieval-Augmented Generation (RAG)
Integrating external data sources with LLMs and NLP techniques for enhanced information retrieval.
Fine-Tuning LLMs
Customizing LLMs for specific financial applications to improve accuracy and relevance.
Text-to-SQL Applications
Translating natural language queries into SQL for querying structured databases using LLMs and NLP.
Retrieval-Augmented Generation (RAG)
Integrating external data sources with LLMs and NLP techniques for enhanced information retrieval.
Fine-Tuning LLMs
Customizing LLMs for specific financial applications to improve accuracy and relevance.
Text-to-SQL Applications
Translating natural language queries into SQL for querying structured databases using LLMs and NLP.
Retrieval-Augmented Generation (RAG)
Integrating external data sources with LLMs and NLP techniques for enhanced information retrieval.
Fine-Tuning LLMs
Customizing LLMs for specific financial applications to improve accuracy and relevance.
Text-to-SQL Applications
Translating natural language queries into SQL for querying structured databases using LLMs and NLP.
Retrieval-Augmented Generation (RAG)
Integrating external data sources with LLMs and NLP techniques for enhanced information retrieval.
Fine-Tuning LLMs
Customizing LLMs for specific financial applications to improve accuracy and relevance.
Text-to-SQL Applications
Translating natural language queries into SQL for querying structured databases using LLMs and NLP.

Evaluation Techniques
Enhancing the verifiability of generated text by LLMs and NLP methods to streamline manual verification processes.
Applications in Analyzing Financial Reports and Alternative Data
Utilizing LLMs and NLP for analyzing financial documents, news, SEC filings, and alternative data such as social media and video content.
Financial Risk Modeling
Applying LLMs and NLP in credit assessment, bankruptcy detection, and other risk modeling scenarios.
Evaluation Techniques
Enhancing the verifiability of generated text by LLMs and NLP methods to streamline manual verification processes.
Applications in Analyzing Financial Reports and Alternative Data
Utilizing LLMs and NLP for analyzing financial documents, news, SEC filings, and alternative data such as social media and video content.
Financial Risk Modeling
Applying LLMs and NLP in credit assessment, bankruptcy detection, and other risk modeling scenarios.
Evaluation Techniques
Enhancing the verifiability of generated text by LLMs and NLP methods to streamline manual verification processes.
Applications in Analyzing Financial Reports and Alternative Data
Utilizing LLMs and NLP for analyzing financial documents, news, SEC filings, and alternative data such as social media and video content.
Financial Risk Modeling
Applying LLMs and NLP in credit assessment, bankruptcy detection, and other risk modeling scenarios.
Evaluation Techniques
Enhancing the verifiability of generated text by LLMs and NLP methods to streamline manual verification processes.
Applications in Analyzing Financial Reports and Alternative Data
Utilizing LLMs and NLP for analyzing financial documents, news, SEC filings, and alternative data such as social media and video content.
Financial Risk Modeling
Applying LLMs and NLP in credit assessment, bankruptcy detection, and other risk modeling scenarios.

Multi-Lingual ESG Identification and Assessment
everaging LLMs and NLP for automated ESG scoring and identifying ESG issues across languages.
Financial Fraud Detection
Using NLP approaches, including those involving LLMs, for detecting fraud in financial transactions.
Enhancing Investor Communication
Utilizing LLMs and NLP to improve investor relations and communication.
Multi-Lingual ESG Identification and Assessment
everaging LLMs and NLP for automated ESG scoring and identifying ESG issues across languages.
Financial Fraud Detection
Using NLP approaches, including those involving LLMs, for detecting fraud in financial transactions.
Enhancing Investor Communication
Utilizing LLMs and NLP to improve investor relations and communication.
Multi-Lingual ESG Identification and Assessment
everaging LLMs and NLP for automated ESG scoring and identifying ESG issues across languages.
Financial Fraud Detection
Using NLP approaches, including those involving LLMs, for detecting fraud in financial transactions.
Enhancing Investor Communication
Utilizing LLMs and NLP to improve investor relations and communication.
Multi-Lingual ESG Identification and Assessment
everaging LLMs and NLP for automated ESG scoring and identifying ESG issues across languages.
Financial Fraud Detection
Using NLP approaches, including those involving LLMs, for detecting fraud in financial transactions.
Enhancing Investor Communication
Utilizing LLMs and NLP to improve investor relations and communication.

Methodologies for Processing Unstructured Data

Techniques to construct structured data from unstructured financial data using LLMs and NLP.

Technical Challenges of LLMs and NLP

Addressing issues such as hallucination, interpretability, and the integration of NLP methods in financial applications.

Prompt Engineering

Optimizing LLM prompts to extract relevant financial insights.

Retrieval-Augmented Generation (RAG)

Integrating external data sources with LLMs and NLP techniques for enhanced information retrieval.

Fine-Tuning LLMs

Customizing LLMs for specific financial applications to improve accuracy and relevance.

Text-to-SQL Applications

Translating natural language queries into SQL for querying structured databases using LLMs and NLP.

Evaluation Techniques

Enhancing the verifiability of generated text by LLMs and NLP methods to streamline manual verification processes.

Applications in Analyzing Financial Reports and Alternative Data

Utilizing LLMs and NLP for analyzing financial documents, news, SEC filings, and alternative data such as social media and video content.

Financial Modeling

Applying LLMs and AI for credit assessment, price forecasting, bankruptcy detection, and other financial modeling scenarios.

Multi-Lingual ESG Identification and Assessment

everaging LLMs and NLP for automated ESG scoring and identifying ESG issues across languages.

Financial Fraud Detection

Using NLP approaches, including those involving LLMs, for detecting fraud in financial transactions.

Enhancing Investor Communication

Utilizing LLMs and NLP to improve investor relations and communication.

Submission
Guidelines

Submission Guidelines

There is no length limit or strict format requirement. While formatting according to ACM guidelines is recommended (see below), we encourage submissions in an open format to allow the authors to focus on content with formatting restrictions. Submissions should be in PDF format and can be directly submitted as they are.

The workshop is non-archival and will not have official proceedings.

Recommended Formatting:

ACM LaTeX Template (sigconf)

ACM Word Template

Additional Requirements

For each submission, at least one author must agree to serve as a reviewer and deliver their reviews on time.

The author list cannot be modified after the initial submission, except before printing.
Any changes after acceptance will be communicated directly with the authors.

Key Dates

Submission Deadline

October 16th, 2024 (Anywhere on Earth)

October 16th, 2024
(Anywhere on Earth)

Author Notification

October 30th, 2024

Workshop

November 14th, 2024 (Thursday, 13:00 - 17:30 Eastern Time)

Submission Process

Submissions are to be made through the OpenReview platform (link to be updated: https://openreview.net/). Authors must submit the paper content (PDF document), title, author names, contact details, and a brief abstract electronically through the workshop’s submission site. At least one author of each accepted paper is required to attend the conference to present their work.

Review Process

This workshop will follow a single-blind review process, where authors’ identities are known to the reviewers, but reviewers’ identities are not disclosed to the authors. There will be no rebuttal period, and all submissions will be treated with strict confidentiality.

Accepted Papers

All accepted papers will be invited to participate in the poster session. Participants are required to print and bring their own posters to the event.
Detailed specifications on poster format and requirements will be provided by the organizers after acceptance.

Selected papers may also be given the opportunity for an oral presentation, subject to scheduling constraints.

Please note that the workshop is non-archival and will not have official proceedings. Only the author names and titles of accepted papers will appear on the website, with no disclosure of any content.

The workshop is non-archival and will not have official proceedings. Only the author names and titles of accepted papers will appear on the website, with no disclosure of any content.

Accepted Papers

Oral Presentation

Scaling Core Earnings Measurement with Large Language Models

Matthew Shaffer

Climate solutions, transition risk, and stock returns

Shirley Lu, Edward Riedl, George Serafeim, Simon Xu

Mental Models and Financial Forecasts

Francesca Bastaniello, Paul Decaire, Marius Guenzel

FinRobot: AI Agent for Equity Research and Valuation with Large Language Models

Tianyu Zhou, Pinqiao Wang, Yilin Wu, Hongyang Yang

AlphaAgents: Large Language Model based Multi-Agents to Build Better Equity Portfolios

(In-person constrained)

Tianjiao Zhao, Jingrao Lyu, Harrison Garber, Stokes Jones, Stefano Pasquali, Dhagash Mehta

Generative AI and Asset Management

(In-person constrained)

Jinfei Sheng, Zheng Sun, Baozhong Yang, Alan Zhang

Poster Presentation

Responsible Evaluation by Design: Measuring AI’s Total Impact in Financial Applications

Jayeeta Putatunda, Daniela Muhaj

Topic Labeling Using Large Language Models

Olga Bogachek

LLMs for the categorisation of SME bank transactions

Brandi Jess, Pietro Alessandro Aluffi, Marya Bazzi, Matt Arderne, Daniel Rodrigues, Kate Kennedy, Martin Lotz

Beyond the Fundamentals: How Media-Driven Narratives Influence Cross-Border Capital Flows

Isha Agarwal, Wentong Chen, Eswar Prasad

AI Democratization, Return Predictability, and Trading Inequality

Anne Chang, Xi Dong, Xiumin Martin, Changyun Zhou

Quantformer: from attention to profit with a quantitative transformer trading strategy

Zhaofeng Zhang, Banghao Chen, Shengxin Zhu, Nicolas Langrené

Enhancing Portfolio Rebalancing Timing Using GPT: A Macroeconomic Indicator Approach

Hyeong jin son, Woo Been Back, Kyeong Soo Shin

Evaluating Financial Sentiment Analysis with Annotators’ Instruction Assisted Prompting: Enhancing Contextual Interpretation and Stock Prediction Accuracy

A M Muntasir Rahman, Ajim Uddin, Guiling Wang

Combining Financial Data and News Articles for Stock Price Movement Prediction by Prompting Large Language Models

Ali Elahi, Fatameh Taghvaei

Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading

Suyeol Yun

Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach

Rian Dolphin, Joe Dursun, Jonathan Chow, Jarrett Blankenship, Katie Adams, Quinton Pike

Leveraging NLP, LLM’s, and Knowledge Graphs for Investor Relations

Odemuno Ogelohwohor, Boyi Qian, Saloni Parekh, Pavitra Kadiyala, Kedi Xu, Cehong Wang, Christopher Policastro, Michael Hunger, Abhay Navale

A Conformal Inference-Based Approach to Credit Score Modeling with Large Language Models

Yuxi Chen, Suwei Ma, Tony Dear

Voting Rationale

Irene Yi, Roni Michaely, Silvina Rubio

AI Exposure without Labor Data: The Expected Impact of AI in Forward-Looking Firm Communications

Jiacheng Liu

Quantifying Qualitative Insights: Leveraging LLMs to Market Predict

Hoyoung Lee, Youngsoo Choi, Yuhee Kwon

The Value of Information from Sell-side Analysts

Linying Lv

Large Language Model Evaluation on Financial Benchmarks

Bing Zhang, Mikio Takeuchi, Ryo Kawahara, Shubhi Asthana, Maruf Hossain, Yada Zhu

Can AI Replace Stock Analysts? Evidence from Deep Learning Financial Statements

G. Nathan Dong

Simulating the Survey of Professional Forecasters

Sophia Kazinnik, Anne Lundgaard Hansen

Under Pressure: Strategic Signaling in Bank Earnings Calls

Sophia Kazinnik, Anne Lundgaard Hansen, Thomas R. Cook, Peter McAdam

Transmission Bias in Financial News

Khaled Obaid, Kuntara Pukthuanthong

Using Large Language Models for Financial Advice

Christian Fieberg, Lars Hornuf, Maximilian Meiler, David Streich

Are Memes a Sideshow: Evidence from WallStreetBets

Bill Qiao, Dexin Zhou

Producing AI Innovation and Its Value Implications

Ambrus Kecskes, Phuong-Anh Nguyen, Roni Michaely, Ali Ahmadi

How Much Should We Trust Large Language Model-Based Measures For Accounting And Finance Research?

Minji Yoo

What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts

Shuaiyu Chen, Clifton Green, Huseyin Gulen, Dexin Zhou

Wisdom or Whims? Decoding Investor Trading Strategies with Large Language Models

Shuaiyu Chen, Lin Peng, Dexin Zhou

Efficiently computing volatility surfaces with minimal arbitrage violations using GANs

Andrew S Na, Meixin Zhang, Justin Wan

An Anatomy of Subjective Expectation

Barry Ke

Quantifying Uncertainty: A New Era of Measurement through Large Language Models

Jessica Gentner, Francesco Audrino, Simon Stalder

Small Language Models for the Democratization of Financial Literacy: Challenges and Opportunities

Tagore Rao Kosireddy, Jeffrey David Wall, Evan Lucas, Xin Li, Jun Dai

BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges

Aleksandr Simonyan

Climate AI for Corporate Decarbonization Metrics Extraction

Aditya Dave, Mengchen Zhu, Dapeng Hu, Sachin Tiwari

Can a GPT4-Powered AI Agent Be a Good Enough Performance Attribution Analyst?

Bruno de Melo

Forecasting Option Returns with News

Jie Cao, Bing Han, Gang Li, Ruijing Yang, Xintong Zhan

Fed Transparency and Policy Expectation Errors: A Text Analysis Approach

Eric Fischer, Rebecca McCaughrin, Saketh Prazad, Mark Vandergon

Prudential Regulation Embedding Transformer (PRET) a domain-adapted model for prudential supervision

Dragos Gorduza, Adam Muhtar

CovenantAI - New Insights into Covenant Violations
(In-person constrained)

Anthony Saunders, Sascha Steffen, Paulina M Verhoff

Counterfactual Analysis via Large Language Models

Ram D. Gopal, Xiao Qiao, Moris S. Strub, Zonghao Yang

Visual Information and AI Divide: Evidence from Corporate Executive Presentations

Sean Cao, Yichen Cheng, Meng Wang, Baozhong Yang, Yusen Xia

One Hundred and Thirty Years of Corporate Responsibility

(In-person constrained)

Joel F. Houston, Sehoon Kim, Boyuan Li

Central Bank Digital Currencies (CBDC) Sentiment Score Analysis of News using Chat GPT-4.0 from January 2018 to June 2024

(In-person constrained)

Renata Ayumi Hokama Penteado Alves

Schedule

11/14 (Thu)

The 1st Workshop on LLMs and Generative AI for Finance

Opening Remarks

1:00 PM - 1:05 PM

Prof. Yoon Kim

MIT, Electrical Engineering and Computer Science

Keynote

1:05 PM - 1:40 PM

Prof. Valeri Nikolaev

University of Chicago,Booth School of Business

University of Chicago, Booth School of Business

“Future of Generative AI in Financial Markets”

Short Talk

1:40 PM - 1:55 PM

Prof. Sean Cao

University of Maryland,Robert H. Smith School of Business

University of Maryland, Robert H. Smith School of Business

“Applied AI in Accounting and Finance: Industry and Academic Applications”

Panel

1:55 PM - 2:30 PM

“Practical Applications of Large Language Models in Finance and Accounting Workflows”

View Panelists

Coffee Break

2:30 PM - 2:40 PM

Keynote

2:40 PM - 3:15 PM

Andrew Lo

MIT, Sloan School of Management

“Generative AI and the Rise of Quantamental Investing”

Short Talk

3:15 PM - 3:30 PM

Armineh Nourbakhsh

J.P. Morgan AI Research Executive Director

“Document AI Workflows and the Challenge of Grounded Generation”

Oral session 1

3:30 PM - 4:00 PM

Matthew Shaffer

“Scaling Core Earnings Measurement with Large Language Models”

Tianyu Zhou, Pinqiao Wang, Yilin Wu, Hongyang Yang

“FinRobot: AI Agent for Equity Research and Valuation with Large Language Models”

Coffee Break

4:00 PM - 4:10 PM

Keynote

4:10 PM - 4:45 PM

Ralph Koijen

University of Chicago, Booth School of Business

“Asset Embeddings”

Short Talk

4:45 PM - 5:00 PM

Miao Liu

Boston College, Carroll School of Management

“Executives vs. Chatbots: Unmasking Insights through Human-AI Differences in Earnings Conference Q&A”

Oral session 2

5:00 PM - 5:30 PM

Francesca Bastaniello, Paul Decaire, Marius Guenzel

“Mental Models and Financial Forecasts”

Shirley Lu, Edward Riedl, George Serafeim, Simon Xu

“Climate Solutions, Transition Risk, and Stock Returns”

Networking &Poster session

Networking &
Poster session

5:30 PM -

Workshop at ICAIF '24

Speakers

Organizing Committee

Panelists

About
the workshop

About
the workshop

Call for papers

Call for papers

Submission
Guidelines

Submission Guidelines

Key Dates

Key Dates

Accepted Papers

Accepted Papers

Schedule

Schedule

11/14 (Thu)

11/14 (Thu)

The 1st Workshop on LLMs and Generative AI for Finance

The 1st Workshop on LLMs and Generative AI for Finance

Organized by Members of the Following Institutions

Sponsored Exclusively by Qube Research & Technologies

Sponsored Exclusively by

Workshop at ICAIF '24

Speakers

Organizing Committee

Panelists

Aboutthe workshop

Aboutthe workshop

Call for papers

Call for papers

SubmissionGuidelines

Submission Guidelines

Key Dates

Key Dates

Accepted Papers

Accepted Papers

Schedule

Schedule

11/14 (Thu)

11/14 (Thu)

The 1st Workshop on LLMs and Generative AI for Finance

The 1st Workshop on LLMs and Generative AI for Finance

Organized by Members of the Following Institutions

Sponsored Exclusively by Qube Research & Technologies

Sponsored Exclusively by

About
the workshop

About
the workshop

Submission
Guidelines