Key Terms in Large Language Models GenAI

Key terms in Large Language Models

1. Key Terms in LLM Prasad Deshmukh

2. LLM "LLM" typically stands for "Large Language Model." These are advanced natural language processing (NLP) models that are trained on vast amounts of text data to understand and generate human-like text. LLMs are often built on transformer-based architectures like GPT (Generative Pre-trained Transformer) and are capable of a wide range of language-related tasks, such as text generation, summarization, translation, question answering, and more . Prasad Deshmukh

3. Transformers The Transformer architecture, introduced in the paper "Attention is All You Need" by Vaswani et al., revolutionized natural language processing (NLP) by utilizing self-attention mechanisms. It consists of encoder and decoder layers, each composed of multi-head self-attention mechanisms and feed-forward neural networks. Transformers have become the foundation for many state-of-the-art language models due to their ability to capture long-range dependencies in sequential data efficiently. Prasad Deshmukh

4. Pre-training Pre-training involves training a model on a large corpus of text data in an unsupervised manner to learn general language representations. During pre-training, the model learns to predict masked tokens in input sequences or to generate coherent text continuations given preceding context. Pre-training is a crucial step in training large language models like GPT, as it allows the model to acquire knowledge of the linguistic structure and semantics of natural language. Prasad Deshmukh

5. Fine-tuning Fine-tuning is the process of further training a pre-trained model on domain-specific data or tasks to adapt its knowledge to specific applications. By fine-tuning on task-specific data with supervised learning techniques, the model can learn to perform tasks such as text classification, summarization, or question answering. Fine-tuning enables the model to leverage its pre-trained knowledge while tailoring its predictions to the target task.