/nlp_course

YSDA course in Natural Language Processing

Primary LanguageJupyter NotebookMIT LicenseMIT

YSDA Natural Language Processing course

  • This is the 2024 version. For previous year' course materials, go to this branch
  • Lecture and seminar materials for each week are in ./week* folders, see README.md for materials and instructions
  • Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
  • Installing libraries and troubleshooting: this thread.

Syllabus

  • week01 Word Embeddings

    • Lecture: Word embeddings. Distributional semantics. Count-based (pre-neural) methods. Word2Vec: learn vectors. GloVe: count, then learn. Evaluation: intrinsic vs extrinsic. Analysis and Interpretability. Interactive lecture materials and more.
    • Seminar: Playing with word and sentence embeddings
    • Homework: Embedding-based machine translation system

  • week02 Text Classification

    • Lecture: Text classification: introduction and datasets. General framework: feature extractor + classifier. Classical approaches: Naive Bayes, MaxEnt (Logistic Regression), SVM. Neural Networks: General View, Convolutional Models, Recurrent Models. Practical Tips: Data Augmentation. Analysis and Interpretability. Interactive lecture materials and more.
    • Seminar: Text classification with convolutional NNs.
    • Homework: Statistical & neural text classification.

  • week03 Language Modeling

    • Lecture: Language Modeling: what does it mean? Left-to-right framework. N-gram language models. Neural Language Models: General View, Recurrent Models, Convolutional Models. Evaluation. Practical Tips: Weight Tying. Analysis and Interpretability. Interactive lecture materials and more.
    • Seminar: Build a N-gram language model from scratch
    • Homework: Neural LMs & smoothing in count-based models.

  • week04 Seq2seq and Attention

    • Lecture: Seq2seq Basics: Encoder-Decoder framework, Training, Simple Models, Inference (e.g., beam search). Attention: general, score functions, models. Transformer: self-attention, masked self-attention, multi-head attention; model architecture. Subword Segmentation (BPE). Analysis and Interpretability: functions of attention heads; probing for linguistic structure. Interactive lecture materials and more.
    • Seminar: Basic sequence to sequence model
    • Homework: Machine translation with attention

  • week05 Transfer Learning

    • Lecture: What is Transfer Learning? Great idea 1: From Words to Words-in-Context (CoVe, ELMo). Great idea 2: From Replacing Embeddings to Replacing Models (GPT, BERT). (A Bit of) Adaptors. Analysis and Interpretability. Interactive lecture materials and more.
    • Homework: fine-tuning a pre-trained BERT model

  • week06 LLMs and Prompting

    • Lecture: Scaling laws. Emergent abilities. Prompting (aka "in-context learning"): techiques that work; questioning whether model "understands" prompts. Hypotheses for why and how in-context learning works. Analysis and Interpretability.
    • Homework: manual prompt engneering and chain-of-thought reasoning

  • week07 Transformer architecture and training

    • Lecture: training tips for transformers; the evolution of transformer architecture from Vaswani et al (2017) to modern LLMs; parameter-efficient fine-tuning (PEFT)
    • Homework: fine-tuning a large language model with PEFT algorithms

  • week08 Reinforcement Learning from Human Feedback

    • Lecture: model alignment, RLHF, case study of InstructGPT and ChatGPT
    • Homework: fine-tune your own language model with RL (using HuggingFace trl)

  • week09 (extra) Domain Adaptation in NLP

    • Lecture: why do domain adaptation? Methods: reweighting, proxy labels, adversarial domain adaptation
    • Optional homework: implement domain adaptation when fine-tuning BERT models

  • week10_ Efficient Inference in NLP

    • Lecture: how NLP models are deployed, a survey of compression and acceleration: quantization, sparsification, ACT & more
    • Practice: implement RTN and GPTQ for 4-bit LLM quantization

  • week11 (extra)_ Retrieval Augmented Language Models

    • Guest lecture: retrieval in LMs, token-level retrieval (KNNLM & more), RAG, RETRO, tools: langchain , HF Agents, open problems

Contributors & course staff

Course materials and teaching performed by