The combination of pre-training and fine-tuning enables LLMs to perform a wide range of natural language processing tasks with high efficiency and adaptability.
Large Language Models (LLMs) undergo two primary phases: pre-training and post-training (often referred to as fine-tuning).
Pre-training: Pre-training is the initial phase where the LLM is exposed to vast amounts of textual data. During this phase, the model learns to predict the next word in a sentence, a process known as unsupervised learning. This involves forming connections between unlabeled and unstructured data, allowing the model to understand language patterns and structures. The pre-training phase leverages transformer architectures, which use self-attention mechanisms to analyze relationships between words and assign weights to determine their relative importance. This phase is resource-intensive, requiring significant computational power and large datasets, sometimes containing trillions of tokens.
Post-training (Fine-tuning): After pre-training, the LLM undergoes fine-tuning, which is a supervised learning process. In this phase, the model is adjusted using a smaller, more specific dataset that is often labeled. Fine-tuning allows the model to perform more targeted tasks and improve its accuracy in specific applications, such as translation, summarization, or question-answering. This phase helps the model adapt to specific domains or tasks by refining its predictions and reducing errors like hallucinations, where the model might generate false or misleading information.
LLM Deep Learning, which stands for Large Language Model Deep Learning, is a type of artificial intelligence (AI) that involves training massive neural networks on vast amounts of data to learn the patterns and structures of human language. The combination of pre-training and fine-tuning enables LLMs to perform a wide range of natural language processing tasks with high efficiency and adaptability.
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