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Concept

Language model

Related lectures (32)

Model Finetuning for NLI

Covers the second assignment for the CS-552: Modern NLP course, focusing on transfer learning and data augmentation.

Modern NLP: Classical Language Models

Covers language models, n-gram models, smoothing techniques, and fixed-context neural models.

Prompting and Alignment

Explores prompting, alignment, and the capabilities of large language models for natural language processing tasks.

Pretraining: Transformers & Models

Explores pretraining models like BERT, T5, and GPT, discussing their training objectives and applications in natural language processing.

Language Models: Fixed-context and Recurrent Neural Networks

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

Natural Language Generation

Explores Natural Language Generation, covering neural models, biases, ethics, and evaluation challenges.

Modern NLP: Introduction

By Antoine Bosselut introduces Natural Language Processing and its challenges, advancements in neural models, and course goals.

Deep Learning for Question Answering

Explores deep learning for question answering, analyzing neural networks and model robustness to noise.

Coreference Resolution

Delves into coreference resolution, discussing challenges, advancements, and evaluation methods.

Ethical Considerations in Natural Language Processing

Explores ethical challenges in NLP systems, including biases, toxicity, privacy, and disinformation.

Data Annotation: Collection and Biases in NLP

Addresses data collection, annotation processes, and biases in natural language processing.

Natural Language Generation: Decoding & Training

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Classical Language Models: Foundations and Applications

Introduces classical language models, their applications, and foundational concepts like count-based modeling and evaluation metrics.

Neural Word Embeddings

Introduces neural word embeddings and dense vector representations for natural language processing.

Modern NLP: From GPT to ChatGPT

Explores the evolution of modern NLP from GPT-2 to GPT-3, emphasizing in-context learning and the development of ChatGPT.

Neural Word Embeddings: Learning Representations for Natural Language

Covers neural word embeddings and methods for learning word representations in natural language processing.

Model Compression: Techniques for Efficient NLP Models

Explores model compression techniques in NLP, discussing pruning, quantization, weight factorization, knowledge distillation, and attention mechanisms.

Language Modelling and Recurrent Neural Networks

Explores language modelling, RNNs, n-gram models, LSTMs, and bidirectional RNNs.

Efficient Methods in Natural Language Processing

Explores the efficiency of training large language models and the environmental impact of AI.

Modern NLP: A Simple Neural Classifier

Introduces the basics of modern NLP, covering word embeddings, neural models, and tasks like sequence labeling and text generation.