LLMs stands for Large Language Models, which refers to a type of artificial intelligence model trained on vast amounts of text data to understand, generate, and respond to human language in a natural and meaningful way. Designers rely on deep learning techniques, particularly neural networks, to create models like OpenAI’s GPT (Generative Pre-trained Transformer). As a result, these models can efficiently process and perform various language-based tasks, including text generation, translation, summarisation, answering questions, and more.
Key characteristics of Large Language Models (LLMs) include:
- Size: LLMs are considered “large” because they contain billions, or even hundreds of billions, of parameters. These parameters, in turn, serve as the adjustable elements within the model.These parameters allow the model to capture complex patterns in language and meaning.
- Training Data: Researchers train LLMs on diverse datasets, including books, websites, articles, and other text sources, giving them a broad understanding of various topics, languages, and linguistic structures..
- Tasks: They can perform a wide range of language-related tasks, including:
- Text generation (like writing articles or stories)
- Language translation
- Question answering
- Text summarisation
- Code generation
- Sentiment analysis
- Pre-trained and Fine-tuned: Researchers typically start by pre-training LLMs on large datasets. After that, they fine-tune these models for specific tasks or industries, such as medical, legal, or technical fields. This process ensures that the models perform optimally in specialised areas.
Examples of Large Language Models (LLMs) include: Virtual Engineers from KFactory®
LLMs have been pivotal in advancing natural language processing (NLP) applications, pushing AI systems to better understand and communicate in human-like ways.