🤖 100 Days of Generative AI — Day 1 — What is an LLM? 🤖
Whether you like it or not, we live through a once-in-a-lifetime technological change known as Generative AI. The advancements in this field are happening so rapidly that we will all need to adopt it sooner or later. I am launching a new series called “100 Days of Generative AI,” where I will regularly post about generative AI and occasionally create related videos. Stay tuned!
A Large Language Model (LLM) is a deep neural network designed to understand, generate, and respond to text in a way that mimics human language. Let’s look at the various components that make an LLM:
Basic Components:
✅ Neural Network: LLMs are based on an artificial brain called a neural network. Think of it as a network of nodes (like neurons) connected together, which can learn patterns from data.
✅ Deep Learning: This subset of machine learning involves neural networks with multiple layers (hence “deep”). More layers can learn more complex patterns.
Training Process:
✅ Massive Amounts of Data: LLMs are trained using vast text data, including books, articles, websites, and more. The model reads and learns from this data to understand language.
✅ Parameters: These are adjustable weights within the neural network. During training, the model fine-tunes these weights to improve its ability to predict the next word in a sequence of text.
Key Characteristics:
“Large” in LLM:
✅ Parameters: LLMs have a huge number of parameters (weights). The more parameters, the more the model can learn and remember.
✅ Immense Dataset: The training data is vast, covering a wide range of topics, which helps the model understand context and nuances in language.
Architecture:
✅ Transformer Architecture: This is the specific design used in many LLMs. It allows the model to selectively focus on different parts of the input text. For example, in the sentence “The cat, which was very playful, chased the ball,” the transformer can understand that “the cat” is the one doing the chasing, even though “the ball” comes much later in the sentence.
Generative AI:
✅ Generative AI: Since LLMs can create text that seems human-written, they are part of a broader category called Generative AI. This means they don’t just understand and analyze text; they can also generate new text based on their learning.
🏁 In summary, LLMs are powerful tools that learn from vast amounts of text data, adjust their internal settings (parameters) during training, and use sophisticated architecture to generate text that sounds natural and coherent.
Reference:
- LEVERAGING LARGE LANGUAGE MODELS TO OPTIMIZE COSTS IN AMAZON WEB SERVICE CLOUD https://www.researchgate.net/publication/384079875_LEVERAGING_LARGE_LANGUAGE_MODELS_TO_OPTIMIZE_COSTS_IN_AMAZON_WEB_SERVICE_CLOUD
- INTELLIGENT LOG MANAGEMENT WITH LARGE LANGUAGE MODELS FOR ANOMALY DETECTION AND RESOLUTION https://www.researchgate.net/publication/383861718_INTELLIGENT_LOG_MANAGEMENT_WITH_LARGE_LANGUAGE_MODELS_FOR_ANOMALY_DETECTION_AND_RESOLUTION
- LEVERAGING LARGE LANGUAGE MODELS (LLMS) FOR ENHANCED DISASTER RECOVERY IN AWS https://www.researchgate.net/publication/384399357_LEVERAGING_LARGE_LANGUAGE_MODELS_LLMS_FOR_ENHANCED_DISASTER_RECOVERY_IN_AWS
