Large Language Models (LLMs) are a subset of artificial intelligence (AI) that specialize in understanding, generating, and human language. They are designed to perform a wide range of tasks involving natural language, such as translation, summarization, question-answering, and generation. LLMs are built using deep learning techniques, typically based on networks, and the "large" aspect refers to the model's size in terms of parameters, which enables them to perform complex language tasks.
LLMs are powered by deep learning architectures, particularly networks, which excel at processing sequential data like text. These models are typically trained in an unsupervised or self-supervised manner, meaning they learn patterns from the text without needing explicit . Transformer's attention mechanisms help focus on relevant parts of a sentence, improving the model's ability to understand context and relationships between words over long sequences. The model is trained on vast amounts of data to learn language structures, forming the base of its .
LLMs can generate coherent, contextually appropriate text based on input , making them useful in various tasks. They can understand and respond to questions by analyzing the context of the input, which aids in answering queries and providing . Additionally, LLMs can translate between languages, recognizing patterns in sentence structure and word usage to offer more natural translations. However, they face challenges, including from the datasets they are trained on, resource requirements, and the lack of true comprehension of the language.
