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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.

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