Concept

Language model

Related lectures (32)

Explores words, tokens, and language models in NLP, covering challenges in defining them, lexicon usage, n-grams, and probability estimation.

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

Delves into advancements and challenges in NLP, along with ethical considerations and potential harms.

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

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

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

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

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

Introduces deep learning concepts for NLP, covering word embeddings, RNNs, and Transformers, emphasizing self-attention and multi-headed attention.

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

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

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

Discusses the ethical implications of NLP systems, focusing on biases, toxicity, and privacy concerns in language models.

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

Delves into pre-training BiLSTM and Transformer models for NLP tasks, showcasing their effectiveness and applications.

Delves into BERT pretraining for transformers, discussing its applications in NLP tasks.

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

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

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

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