{"id":4162,"date":"2025-08-06T10:25:02","date_gmt":"2025-08-06T10:25:02","guid":{"rendered":"https:\/\/uplatz.com\/blog\/?p=4162"},"modified":"2025-08-06T10:25:02","modified_gmt":"2025-08-06T10:25:02","slug":"llms-flashcards","status":"publish","type":"post","link":"https:\/\/uplatz.com\/blog\/llms-flashcards\/","title":{"rendered":"LLMs Flashcards"},"content":{"rendered":"
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Large Language Models (LLMs)<\/div>\n
Deep learning models trained on massive text datasets to understand and generate human language.<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
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Transformer Architecture<\/div>\n
The backbone of LLMs, leveraging self-attention mechanisms for language understanding.<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
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Self-Attention<\/div>\n
A mechanism allowing the model to weigh the importance of each word relative to others in a sentence.<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
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GPT<\/div>\n
Generative Pre-trained Transformer models (GPT) excel in text generation and language tasks.<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
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BERT<\/div>\n
Bidirectional Encoder Representations from Transformers, optimized for understanding context in both directions.<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
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Tokenization<\/div>\n
The process of breaking down text into smaller units (tokens) for input into LLMs.<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
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Fine-Tuning<\/div>\n
Adjusting a pre-trained LLM on a smaller, domain-specific dataset for better performance.<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
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Prompt Engineering<\/div>\n
Crafting effective input prompts to guide the output of LLMs toward desired responses.<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
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RAG (Retrieval-Augmented Generation)<\/div>\n
Combining retrieval from external sources with generation from LLMs to improve factual accuracy.<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
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LoRA<\/div>\n
Low-Rank Adaptation: a technique for efficient fine-tuning of large models by freezing base weights.<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
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Hallucination<\/div>\n
When LLMs generate outputs that are plausible-sounding but factually incorrect or fabricated.<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
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Model Evaluation<\/div>\n
Metrics such as BLEU, ROUGE, perplexity, and human feedback used to assess LLM performance.<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Large Language Models (LLMs) Deep learning models trained on massive text datasets to understand and generate human language. Transformer Architecture The backbone of LLMs, leveraging self-attention mechanisms for language understanding. 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