This lecture slideshow explores the world of Large Language Models (LLMs), detailing their architecture, training, and application. It begins by explaining foundational concepts like recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) before moving on to Transformers, the architecture behind modern LLMs. The presentation then discusses pre-training, fine-tuning, and various parameter-efficient techniques for adapting LLMs to downstream tasks. Finally, the slideshow addresses critical challenges facing LLMs, including safety concerns, bias, outdated knowledge, and evaluation difficulties.

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