DS357 Large Language Models

• 3-0-0-4-4

• This course introduces the foundations and evolution of Large Language Models (LLMs), covering key concepts from neural language models to transformer architectures. It explores major LLM architectures such as GPT, BERT, and encoder–decoder models, along with tokenization and decoding strategies. The course further examines advanced topics including fine-tuning, scaling, retrieval-augmented generation, and prompt engineering. Practical aspects focus on real-world applications, agents, and tool integration. Key challenges such as bias, hallucination, and alignment are also discussed. The course concludes with emerging trends including multimodal and next-generation LLM systems.

Unit 1: Foundations of Large Language Models

Introduction to Language Models, Fundamentals of language models: History, evolution, and significance in NLP, Word Embeddings, Neural LMs, Transformer Architecture, Transformer basics: Self-attention mechanism, Encoder-Decoder structure, Encoder layers and encoder stack, Core Components of Transformers, Position encoding, batch normalization, and layer normalization, Teacher forcing and masked attention

Unit 2: Architectures of Large Language Models

Decoder-only Large Language Models, Detailed study of GPT architecture (Causal Language Model), Pre-training vs. fine-tuning for downstream applications, Decoding strategies: Greedy, Beam Search, Top-k, and Top-p, Encoder-only Large Language Models, BERT architecture (Masked Language Model) and training objectives, Applications and adaptation for various NLP tasks, Tokenization Techniques, Sub-word tokenization, Byte Pair Encoding, WordPiece, and SentencePiece, Introduction to Small Language Models, Overview and applications of Small Language Models in resource-constrained settings

Unit 3: Advanced Architectures and Adaptation of LLMs

Encoder-Decoder Models, Introduction to models like BART and T5, Understanding the Text-to-Text framework and zero-shot learning, Advanced Model Components, Pre-training strategies, scaling laws, and instruction fine-tuning, Advanced attention mechanisms and the Mixture of Experts approach, Parameter-Efficient Fine-Tuning (PEFT), Techniques for efficient adaptation and inference, Advantages of PEFT in optimizing LLMs for specific tasks, Efficient Model Adaptation, Retrieval and tool augmentation for enhanced model capabilities, Prompt Engineering, Chains, Memory and Agents

Unit 4: LLMs in Practice and Future Directions

Addressing Challenges in LLMs, Bias, toxicity, hallucination, and alignment in LLMs, Interpreting LLMs: Understanding the inner workings and output reasoning, Specialized Models and Emerging Trends, Multimodal LLMs, Vision-Language models and long-context LLMs, Model editing, self-evolving LLMs, and efficient inference, Future of LLMs, Ethical implications, evolving capabilities, and emerging applications in LLMs

• Lecture 1: Course Introduction

• Lecture 2: Introduction to Language Model: Statistical Language Modeling

• Lecture 3: Word Representation: Word2vec

• Lecture 4: Word Representation: GloVe

• Lecture 5: Neural LMs: RNNs, LSTMs, and GRUs

• Lecture 6: Neural LMs: Seq-to-Seq and Attention

• Lecture 7: Introduction to Transformer: Self-Attention and Multi-Head Attention

• Lecture 8: Introduction to Transformer: Positional Encoding and Layer Normalization

• Lecture 9: Pre-training Strategies

• Lecture 10: Advanced Attention Mechanisms

• Midsem

• Lecture 11: Tokenization Strategies

• Lecture 12: Mixture of Experts & Scaling Laws

• Lecture 13: Pre-training of Causal LMs, In-context Learning, & Instruction Tuning

• Lecture 14: Alignment of Language Models: Reward Maximization

• Lecture 15: Parameter Efficient Fine-Tuning (PEFT)

• Lecture 16: Efficient LLM Decoding

• Lecture 17: Retrieval-based Language Models

• Lecture 18: Multimodal Models

• Datasets (for experiments & demos)

  • Wikipedia Corpus

  • Common Crawl (via filtered subsets like C4)

  • BookCorpus

  • OpenWebText

  • GLUE / SuperGLUE Benchmarks

  • SQuAD (QA tasks)

  • CNN/DailyMail (summarization)

  • IndicCorp / AI4Bharat datasets (for Indian context)

  • Kaggle NLP datasets (sentiment, classification, etc.)

• Libraries & Frameworks

  • Hugging Face Transformers

  • Hugging Face Datasets

  • PyTorch

  • TensorFlow / Keras (optional)

  • SentenceTransformers

  • spaCy / NLTK (for basics)

• Pretrained Models (for usage & demos)

  • GPT family (via APIs or open models)

  • BERT, RoBERTa

  • T5, BART

  • LLaMA / Mistral (open-weight models)

  • IndicBERT / multilingual models

• Tools & Platforms

  • Google Colab / Jupyter Notebook

  • Hugging Face Hub

  • OpenAI API / similar LLM APIs

  • Kaggle Notebooks

  • GitHub (model/code access)

• LLM Application Frameworks

  • LangChain

  • LlamaIndex

  • Haystack

  • Vector Databases (for RAG)

  • FAISS

  • ChromaDB

  • Pinecone (intro level)

• Evaluation & Experimentation

  • BLEU, ROUGE (basic metrics)

  • Hugging Face Evaluate

  • Prompt evaluation (manual + automated)

• Deployment / Lightweight Tools (optional exposure)

  • Gradio (quick demos)

  • Streamlit (simple apps)

Midterm (25%)

14 Quizzes (28%)

Group Project: Implementation (10%), Knowledge (10%), Analysis (15%)

Attendance (12%)

Textbooks:

• Introduction to Large Language Models: Generative AI for Text by Prof. Tanmoy Chakraborty

Additional Resources:

• https://onlinecourses.nptel.ac.in/noc26_cs88/preview

• Speech and Language Processing, Dan Jurafsky and James H. Martin

• Foundations of Statistical Natural Language Processing, Chris Manning and Hinrich Schütze

• Natural Language Processing, Jacob Eisenstein

• A Primer on Neural Network Models for Natural Language Processing, Yoav Goldberg