[Deadline extension][CFP] 2nd International Workshop on Generative AI for Textual Document Analysis (GENAIDOC)

[boutalb...@gmail.com]

2nd International Workshop on Generative AI for Textual Document Analysis (GENAIDOC)

https://sites.google.com/view/genaidoc-workshop-fllm-2025

As part of the 3rd International Conference on Foundation and Large Language Models (FLLM2025)

https://fllm-conference.org/2025/index.php

November 25 to November 28, 2025, Vienna, Austria

Context:

Nowadays, the volume of textual data being generated is unprecedented. From social media posts, news articles, and academic papers to customer reviews, emails, and business documents, the sheer quantity of text data is growing exponentially. Traditional methods of analyzing this vast amount of data often fall short in terms of scalability, accuracy, and efficiency. In this context, Generative AI (GenAI) is revolutionizing the field of Natural Language Processing (NLP) by enabling the creation of highly sophisticated Large Language Models (LLMs) that can generate, understand, and manipulate human language. GenAI models like GPT-4 and BERT are at the forefront of these advancements from chatbots to automated content creation. This workshop aims to provide participants with a deep understanding of LLMs, its applications in NLP, and the ethical considerations involved. GenAI models are designed to handle and process enormous datasets, making them ideal for textual document analysis. These models leverage advanced machine learning techniques to understand, interpret, and generate human-like text, allowing for more nuanced and comprehensive analysis. By using LLMs, we can uncover insights and patterns that would be impossible to detect using conventional methods. 

Objective:

This workshop is designed to provide a comprehensive understanding of how LLMs can be leveraged for textual document analysis. Participants will gain hands-on experience and theoretical knowledge about the applications, capabilities, and limitations of GenAI models in the context of analyzing textual data. The workshop will cover various techniques and tools, practical implementation, and the latest advancements in the field. The GENAIDOC workshop aims to bring together an area for experts from industry, science, and academia to exchange ideas and discuss ongoing research in natural language processing and GenAI for textual document analysis.

Novelty for this edition:

After the success of GENAIDOC 2024 at FLLM’24,  in this edition of GENAIDOC will further explore a fast-emerging yet underformalized area at the intersection of large language models (LLMs), document understanding, and multimodal AI. The proposed extension focuses on the design of effective prompting strategies for extracting, interpreting, and aligning textual and visual information from real-world documents such as scanned PDFs, structured forms, and tabular data. 

The new focus will delve into the design of prompting strategies that enable effective extraction, interpretation, and alignment of textual and visual elements from scanned documents, PDFs, structured forms, and tabular data. This includes analyzing how prompts influence the performance of LLMs when processing OCR-based content, determining the best approaches for handling visual structures such as tables and layout elements, and studying how textual and visual modalities can be integrated or separated during inference. The topic will also cover the development of standardized prompt templates adapted to industrial contexts such as invoice and document processing. This direction aims to combine technical depth with real-world applicability, enhancing both academic and practical contributions to the field.

Topics of interests:

This workshop invites submissions with high-quality works that are related, but are not limited, to the topics below:

• • Prompts to extract textual information

  • Prompts to extract visual information

  • Prompts to extract data from tables

  • Prompts for specific documents type

  • Prompts to classify documents

  • Text classification

  • Automatic document summarization

  • Automatic machine translation

  • Sentiment analysis

  • Text generation

  • Deep learning for NLP

  • Reinforcement Learning for NLP

  • Unsupervised Learning for NLP

  • Speaker identification

  • Speech recognition

  • Speech to Text

  • Text detection and recognition from images

  • Question Answering systems

  • Transfer Learning for NLP

  • Active Learning for NLP

  • Real-life and industrially relevant NLP applications

    • Email filtering

    • invoice information extraction

    • News generation

    • Meeting analysis

    • CVs analysis and classification 

Submission: 

Papers submitted for review should conform to IEEE specifications. Manuscript templates can be downloaded from IEEE website. The maximum length of papers is 8 pages. All the papers will go through the double-blind peer review process. Authors’ names and affiliations should not appear in the submitted paper. Authors’ prior work should be cited in the third person. Authors should also avoid revealing their identities and/or institutions in the text, figures, links, etc.

Authors should also ensure that their identity is not revealed indirectly by citing their previous work in the third person and omitting acknowledgments until the camera-ready version. Papers have to be submitted via the workshop's EasyChair submission page.

Please include in the paper title "Full paper: Title" or "Short paper: Title" to precise the contribution type. At least one author of each accepted paper must register for the workshop, in order to present the paper. For further instructions, please refer to the FLLM 2025 page.

Important dates: 

• Submission Deadline: August 31, 2025  September 21st, 2025

• Decisions Announced: September 30, 2025 October 10th, 2025

• Camera Ready Deadline: October 08, 2025 October 25th, 2025

Workshop: To be announced 

Publication: 

Accepted papers will be submitted to IEEEXplore for possible publication.

Workshop Chairs

Rim Hantach, Engie, France

Rafika Boutalbi, Aix-Marseille University, France

Karima Boutalbi, Cegedim Business Services, France