Large language models (LLMs) are deep learning models trained on extensive text data to comprehend and generate human-like text. They employ transformer architecture, possessing billions or trillions of parameters to predict subsequent tokens in a sequence. LLMs excel in various natural language processing (NLP) tasks, including text generation, summarization, translation, and question answering, often utilizing few-shot or zero-shot learning approaches. The architecture consists of key components such as an encoder-decoder structure, though many models are predominantly decoder-only, and a self-attention mechanism that enables them to focus on relevant parts of the input text.
Foundational language models, such as large language models (LLMs), provide capabilities in various natural language processing (NLP) tasks by leveraging vast datasets during training.
The transformer architecture is fundamental to LLMs, characterized by an encoder-decoder structure, where many models adapt this to primarily focus on decoder-based design.
#large-language-models #transformer-architecture #natural-language-processing #machine-learning #ai-tasks
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