Degree Level Course

Large Language Models

Understanding the Transformer architecture Understanding the concept of pretraining and fine-tuning language models Compare and contrast different types of tokenizers like BPE, wordpiece, sentencepiece Understanding different LLMs architectures: encoder-decoder, encoder-only, decoder-only Exploring common datasets like C4,mc4,Pile, Stack and so on Addressing the challenges of applying vanilla attention mechanisms for long range context windows. Apply different types of fine-tuning techniques to fine-tune large language models

Taught by Prof. Mitesh M.Khapra

Code BSDA5004

Credits 4 Credits

Type Elective

Prerequisites

BSCS3004

12-Week Roadmap

Course Structure & Syllabus

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For details of standard term assessment timelines and exam structures, visit our Academics page.

WEEK 1

Transformers: Introduction to transformers - Self-attention - cross- attention-Masked attention-Positional encoding

WEEK 2

A deep dive into number of parameters, computational complexity and FLOPs- Introduction to language modeling

WEEK 3

Causal Language Modeling: What is a language model?- Generative Pretrained Transformers (GPT) - Training and inference

WEEK 4

Masked Language Modeling : Bidirectional Encoder Representations of Transformers (BERT) - Fine-tuning - A deep dive into tokenization: BPE, SentencePiece, wordpiece

Reading List

Prescribed Books & References

Research papers, articles

Faculty & Experts

About the Instructors

Prof. Mitesh M.Khapra

Associate Professor , Department of Computer Science and Engineering , IIT Madras

Mitesh M. Khapra is an Associate Professor in the Department of Computer Science and Engineering at IIT Madras and is affiliated with the Robert Bosch Centre for Data Science and AI. He is also a co-founder of One Fourth Labs, a startup whose mission is to design and deliver affordable hands-on courses on AI and related topics. He is also a co-founder of AI4Bharat, a voluntary community with an aim to provide AI-based solutions to India-specific problems. His research interests span the areas of Deep Learning, Multimodal Multilingual Processing, Natural Language Generation, Dialog systems, Question Answering and Indic Language Processing. Prior to IIT Madras, he was a Researcher at IBM Research India for four and a half years, where he worked on several interesting problems in the areas of Statistical Machine Translation, Cross Language Learning, Multimodal Learning, Argument Mining and Deep Learning. Prior to IBM, he completed his PhD and M.Tech from IIT Bombay in Jan 2012 and July 2008 respectively.During his PhD he was a recipient of the IBM PhD Fellowship (2011) and the Microsoft Rising Star Award (2011). He is also a recipient of the Google Faculty Research Award (2018), the IITM Young Faculty Recognition Award (2019) and the Prof. B. Yegnanarayana Award for Excellence in Research and Teaching (2020).

Other Courses by Instructor:

BSCS3004 • Deep Learning BSDA5013 • Deep Learning Practice

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