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.

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

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

Chatbot

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.

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 2024 page.