Unleashing the Power of Large Language Models: A Hands-On Tutorial

Santra, P., Ghosh, M., Mukherjee, S., Ganguly, D. , Basuchowdhuri, P. and Naskar, S. K. (2024) Unleashing the Power of Large Language Models: A Hands-On Tutorial. In: 15th Annual Meeting of the Forum for Information Retrieval Evaluation, Panjim, Goa, India, 15-18 Dec 2023, pp. 149-152. ISBN 9798400716324 (doi: 10.1145/3632754.3632943)

Abstract

LLMs have opened up possibilities for advancing the state-of-the-art in natural language processing (NLP). In this tutorial, we present the audience with an introduction to LLMs and the associated challenges. The tutorial is structured in the following manner. First, we provide a brief preface that outlines the fundamental principles of NLP, following which, we explore the area of distributional representation learning for NLP. Then, we delve into the essential component of transformer-based pretrained language models. We then follow this up with the concept of prompt learning or in-context learning (ICL) and discuss how it is emerging as a popular methodology replacing the conventional supervised learning workflow comprised of pretraining and fine-tuning. We outline the research challenges in ICL, which usually involves finding the correct set of examples and contexts for the purpose of guiding the LLM decoder towards effective predictions. Afterwards, a hands-on coding and demonstration session will be carried out to impart practical knowledge about LLMs and ICL to the tutorial participants.

• Item Type: Conference Proceedings
• Additional Information: Published in Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation: 149–152.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ganguly, Dr Debasis
• Authors: Santra, P., Ghosh, M., Mukherjee, S., Ganguly, D., Basuchowdhuri, P., and Naskar, S. K.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation
• Publisher: ACM
• ISBN: 9798400716324