Large Language Models (LLMs) 

• A Large Language Model (LLM) is an artificial intelligence program capable of understanding, interpreting and generating human-like text.
• Called “large” because they are trained on massive datasets containing billions of words.
• Built on machine learning, specifically transformer-based neural networks, which are highly effective at processing sequences of text.
• Function like systems that have learned language patterns by being exposed to enormous volumes of example sentences.

How LLMs Work

• Trained on internet-scale text data, often thousands or millions of gigabytes.
• May also use curated datasets to improve language quality and reduce noise or bias.
• Use deep learning to analyse patterns in unstructured text.
• Neural networks (with multiple layers) learn relationships between words, sentences, and meanings.
• Transformer models rely on self-attention, allowing them to understand context and relationships between distant words in a sentence.
• After base training, LLMs may be fine-tuned or prompt-tuned for specific tasks like summarization or translation.

Uses of LLMs

• Perform tasks such as answering questions, summarising content, translation, writing and editing text, coding support, and data search.
• Used by businesses for productivity, customer engagement, recommendations, automation, and innovation.
• Serve as the foundation behind major generative AI tools like ChatGPT, Claude, Microsoft Copilot, Gemini, and Meta AI.
• Newer models are multimodal, meaning they can process text, images, audio, and video → hence called foundation models.

Key Concepts

• Machine Learning: AI systems learn from examples and identify patterns.
• Deep Learning: Models self-learn complex patterns without human instructions.
• Neural Networks: Multi-layered node systems that pass information and adjust weights.
• Transformer Models: Use self-attention to understand relationships in sequences and capture context effectively.

Challenges Associated with LLMs

• Require extremely high computing power and specialised hardware.
• Raise ethical issues: misinformation, bias, data privacy concerns.
• Sometimes struggle with complex reasoning or understanding real-world context.
• Depend heavily on the quality and diversity of training data.