Learn · Introduction to Large Language Models (LLMs)
Lecture 34 of 37Week 11
Lec 34 : Knowledge and Retrieval: Modeling Hierarchies
Prof. Tanmoy Chakraborty and Prof. Soumen Chakraborti · IIT Delhi IIT Bombay
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- Introduction to Large Language Models (LLMs), IIT Delhi IIT Bombay (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge. Coordinated by IIT Delhi.
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All the lectures
- Lec 01 : Introduction and Recent Advances
- Lec 02 : Introduction to Natural Language Processing
- Lec 03 : Introduction to Statistical Language Models
- Lec 04 : Statistical LM: Advanced Smoothing and Evaluation
- Lec 05 : Introduction to Deep Learning
- Lec 06 : Introduction to PyTorch
- Lec 07 : Word Representation: Word2Vec & fastText
- Lec 08 : Word Representation: GloVe
- Lec 09 : Tokenization Strategies
- Lec 10 : Neural Language Models: CNN & RNN
- Lec 11 : Neural Language Models: LSTM & GRU
- Lec 12 : Sequence-to-Sequence Models
- Lec 13 : Decoding Strategies
- Lec 14 : Attention in Sequence-to-Sequence Models
- Lec 15 : Introduction to Transformer: Self & Multi-Head Attention
- Lec 16 : Introduction to Transformer: Positional Encoding and Layer Normalization
- Lec 17 : Implementation of Transformer using PyTorch
- Lec 18 : Pre-Training Strategies: ELMo, BERT
- Lec 19 : Pre-Training Strategies: Encoder-decoder and Decoder-only Models
- Lec 20 : Introduction to HuggingFace
- Lec 21 : Instruction Tuning
- Lec 22 : Prompt-based Learning
- Lec 23 : Advanced Prompting and Prompt Sensitivity
- Lec 24 : Alignment of Language Models-I
- Lec 25 : Alignment of Language Models-II
- Lec 26 : Knowledge and Retrieval: Knowledge Graph
- Lec 27 : Knowledge and Retrieval: Knowledge Graph Completion and Evaluation
- Lec 28 : Knowledge and Retrieval: Translation and Rotation Models
- Lec 29 : Parameter Efficient Fine-Tuning (PEFT)
- Lec 30 : Quantization, Pruning & Distillation
- Lec 31 : An Alternate Formulation of Transformers: Residual Stream Perspective
- Lec 32 : Interpretability Techniques
- Lec 33 : Knowledge and Retrieval: Multiplicative models
- Lec 34 : Knowledge and Retrieval: Modeling Hierarchies
- Lec 35 : Knowledge and Retrieval: Temporal Knowledge Graphs
- Lec 36 : Responsible LLMs
- Lec 37 : Conclusion: Expert Panel Discussion