GENAIDOC Workshop: Leveraging Large Language Models for Textual Document Analysis
The GENAIDOC workshop, held as part of the 2nd International Conference on Foundation and Large Language Models (FLLM2024) in Dubai, UAE, focuses on the use of Generative AI (GenAI) for textual document analysis. This workshop aims to provide a comprehensive understanding of how large language models (LLMs) can be leveraged for analyzing textual data, including techniques and tools, practical implementation, and the latest advancements in the field. 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 GENAIDOC workshop brings together experts from industry, science, and academia to exchange ideas and discuss ongoing research in natural language processing and GenAI for textual document analysis.
Topics of interest include 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, and real-life and industrially relevant NLP applications such as email filtering, chatbot, news generation, meeting analysis, and CVs analysis and classification.
Papers submitted for review should conform to IEEE specifications and go through the double-blind peer review process. The maximum length of papers is 8 pages, and at least one author of each accepted paper must register for the workshop to present the paper. For further instructions, please refer to the FLLM 2024 page.
Important dates: Submission Deadline – September 10, 2024; Decisions Announced – October 05, 2024; Camera Ready Deadline – October 25, 2024; Workshop – To be announced.
Accepted papers will be submitted to IEEEXplore for possible publication.