Efficient Methods in Natural Language Processing

Related lectures (30)

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Covers ethical considerations in NLP, focusing on bias, toxicity, and misinformation.

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

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

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

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

Covers the scaling of language models, focusing on training efficiency and deployment considerations.

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

Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.

Explores building OS for heterogeneous hardware, data movement efficiency, AI advancements, and NLP challenges.

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