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How a Large Language Model Works | IABAC

A large language model processes vast text data to learn language patterns using neural networks called transformers. It predicts and generates text based on context, enabling tasks like writing, summarizing, and conversation through pattern recognition and probability-based word prediction.

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How a Large Language Model Works | IABAC

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  1. How a Large Language Model Works iabac.org

  2. Introduction to Large Language Models (LLMs) A Large Language Model (LLM) is an AI system trained to understand and generate human language. It learns patterns from large text datasets to predict the next word in a sequence. Examples: GPT-5, Claude, Gemini, LLaMA. Core idea: mimic how humans use language by identifying context and structure. iabac.org

  3. Training Data and Learning Process Trained on massive text datasets — books, articles, websites, and code. Uses unsupervised learning — learns without explicit labels. Objective: minimize prediction error when generating text. Learns grammar, facts, reasoning, and context through repeated pattern recognition. iabac.org

  4. Model Architecture Built on the Transformer model — introduced by Google in 2017. Uses attention mechanisms to focus on important words in a sentence. Handles long-range dependencies in text efficiently. Layers of interconnected nodes process and refine information at scale. iabac.org

  5. Inference and Response Generation During use, the model predicts the most likely next word based on context. Uses probability distributions to select coherent text sequences. Can summarize, translate, code, or converse based on user prompts. Output depends on prompt clarity and model fine-tuning. iabac.org

  6. Applications and Limitations Chatbots, content creation, research assistance, coding, customer support. Limitations: May produce inaccurate or biased information. Requires large computational resources. Needs human oversight for reliability and ethics. iabac.org

  7. Thank you Visit: www.iabac.org iabac.org

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