Lectures
You can download the lecture slides here.
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18. Efficient adaptation
[slides]
- Brown et al. (2020). Language Models Are Few-Shot Learners
- Hu et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models
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16. Lab 8 Constructed languages
[slides]
- Taguchi & Sproat. (2025). IASC: Interactive Agentic System for ConLangs
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15. Pre/Post-training
[slides]
- SLP, Chapter 9
- Chung et al. (2022). Scaling Instruction-Finetuned Language Models
- Wang et al. (2022). How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
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14. Lab 7 Ollama
[slides]
- Huang et al. (2018). Music Transformer; Cool demo.
- Smith. (2020). Contextual Word Representations.
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13. Transformers
[slides]
- SLP, Chapter 8
- Devlin et el. (2019). BERT: Pre-training of Deep Bidirectional Transformers
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9. Language modeling, RNNs
[slides]
- SLP, Chapter 3
- SLP, Chapter 13
- Sak et al. (2014). Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling
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7. Dependency parsing
[slides]
- SLP, Chapter 19
- de Marneffe et al. (2021). Universal Dependencies
- Chen & Manning (2014). A fast and accurate dependency parser using neural networks
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5. Neural network
[slides]
- SLP, Chapter 6
- Pennington, Socher, & Manning (2014). Natural language processing (almost) from scratch
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4. Lab 2 Word vectors
[slides] [colab]
- Levy, Goldberg, & Dagan (2015). Improving Distributional Similarity in Word Embeddings
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3. Word vectors
[slides]
- SLP, Chapter 5, Chapter I
- Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space
- Pennington, Socher, & Manning (2014). GloVe: Global Vectors for Word Representation
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1. Introduction
[slides]
- Manning, C. D. (2022). Human language understanding & reasoning
- Bommasani, R. et al. (2021). On the opportunities and risks of foundation models
Acknowledgment: These course slides are based on materials from CS224N: NLP with Deep Learning @ Stanford University.