Tag: large language models

Parameter Counts in Large Language Models: Why Size and Scale Matter for Capability
Parameter Counts in Large Language Models: Why Size and Scale Matter for Capability

Tamara Weed, Dec, 20 2025

Parameter count in large language models determines their reasoning power, knowledge retention, and task performance. Bigger isn't always better-architecture, quantization, and efficiency matter just as much as raw size.

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How Large Language Models Communicate Uncertainty to Avoid False Answers
How Large Language Models Communicate Uncertainty to Avoid False Answers

Tamara Weed, Dec, 19 2025

Large language models often answer confidently even when they're wrong. Learn how new methods detect when they're out of their depth-and how to make them communicate uncertainty honestly to build real trust.

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How Large Language Models Learn: Self-Supervised Training at Internet Scale
How Large Language Models Learn: Self-Supervised Training at Internet Scale

Tamara Weed, Sep, 30 2025

Large language models learn by predicting the next word across trillions of internet text samples using self-supervised training. This method, used by GPT-4, Llama 3, and Claude 3, enables unprecedented language understanding without human labeling - but comes with major costs and ethical challenges.

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Reasoning in Large Language Models: Chain-of-Thought, Self-Consistency, and Debate Explained
Reasoning in Large Language Models: Chain-of-Thought, Self-Consistency, and Debate Explained

Tamara Weed, Aug, 9 2025

Chain-of-Thought, Self-Consistency, and Debate are transforming how large language models solve complex problems. Learn how these methods work, when to use them, and why they’re becoming essential in AI systems by 2025.

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