Basics of LLM Run your First Model

What are LLMs?
Large Language Models are a subset of artificial intelligence focused on understanding and generating human language. These models leverage deep learning-a branch of ML that relies on neural networks-to interpret and produce text, code, images, and even complex instructions. Unlike traditional predictive ML models, which often focus on binary classification (e.g., "cat" or "not a cat"), LLMs are designed to generate new content, making them a subset of generative AI.
Evolution of LLMs
LLMs have grown exponentially since their inception, with models like OpenAI's GPT series evolving from a few hundred million parameters to over a trillion in recent versions. Parameters, analogous to neurons in the human brain, determine the model's capacity to "understand" and generate nuanced responses. This growth in parameters has led to unprecedented capabilities in text generation, summarization, language translation, and code generation, positioning LLMs as indispensable tools in various fields.
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Understanding LLM Architectures: Encoder-Decoder Structure
LLMs generally consist of two main components: *encoders* and **decoders**. Each plays a unique role in understanding and generating language.
**Encoder**: The encoder breaks down the input text into tokens (e.g., words or subwords), which are then converted into embeddings (mathematical representations). The encoder processes these tokens, correlating and understanding the context by mapping relationships between them using attention mechanisms. This is akin to reading and understanding the prologue of a story to grasp its theme and direction.
**Decoder**: Once the input context is embedded, the decoder uses it to generate the next word or token in a sequence, based on the preceding tokens. This process of next-word prediction underpins the model's text generation, allowing it to write sentences, answer questions, or complete stories coherently.
Some LLMs, like GPT models, use a decoder-only architecture focused on text generation, while others may integrate both encoders and decoders for tasks requiring comprehension and output synthesis.
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Generative vs. Predictive Models
**Predictive Models**: These models classify input data into specific outcomes, such as recognizing images or categorizing text. They're widely used in applications like Google Photos or spam detection.
**Generative Models**: LLMs are primarily generative, which means they not only classify data but can create new content based on the input. For instance, given the beginning of a story, a generative model can predict and generate the remaining parts. This capability extends to images, voice, and even code generation, offering unprecedented flexibility for tasks such as content creation and interactive dialogue systems.
Hallucinations and Reliability
A common limitation with LLMs is "hallucination," where the model generates information that may be plausible but is factually incorrect or fabricated. For security researchers, understanding this limitation is essential, as LLMs may output misleading or incorrect information if not guided accurately.