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LLMs are foundation models trained on very large text datasets for language generation and other natural language tasks.
Large Language Models (LLMs) are machine learning models, usually based on neural networks and the transformer architecture, trained on vast collections of text to perform natural language processing tasks. They can generate, summarize, translate, classify, and analyze text, and they power modern conversational systems such as ChatGPT. GPT models are a major type of LLM that are pretrained to predict the next token and are often fine-tuned to follow instructions or act as assistants. LLM development progressed from statistical n-gram models and recurrent neural networks to word embeddings, sequence-to-sequence systems, transformers, and increasingly capable decoder-only models. LLM development involves collecting and preprocessing data, tokenizing text into numerical units, removing low-quality or harmful content, and sometimes incorporating synthetic data. Models are pretrained, commonly through next-token prediction, and then fine-tuned using supervised instruction tuning, reinforcement learning from human feedback, or preference-optimization methods. During inference, a trained model generates outputs from user inputs. Important technical concepts include attention, context windows, mixture-of-experts architectures, parameter scaling, quantization, prompting, and multimodal processing. Despite their capabilities, LLMs can produce inaccurate or biased outputs, reflect problems in their training data, and require substantial computing resources; evaluation therefore considers accuracy, reasoning, alignment, fairness, and safety.
LLMs are foundation models trained on very large text datasets for language generation and other natural language tasks.
Most modern LLMs use transformers and attention mechanisms, although alternative architectures such as recurrent models also exist.
GPT-style models are pretrained to predict the next token and can be fine-tuned to follow instructions, participate in dialogue, or perform specialized tasks.
The LLM pipeline includes tokenization, dataset cleaning, pretraining, fine-tuning, inference, and optional extensions such as tool use and retrieval.
Model capabilities and costs are influenced by parameter count, context length, precision, quantization, and architectures such as mixture of experts.
LLMs can be adapted through prompting methods such as few-shot prompting, and some can process multiple modalities including text, images, audio, or 3D data.
Major limitations include hallucinations, bias, inaccurate or toxic outputs, memorization risks, safety concerns, and high training and inference costs.
A machine learning model trained on extensive text data to understand and generate language.
A neural network architecture that uses attention to model relationships among tokens in a sequence.
The process of converting text into numerical tokens that a model can process.
A tokenization method that repeatedly merges frequently occurring adjacent symbol pairs to build a vocabulary.
The initial training stage in which a model learns general language patterns from large datasets.
An objective in which a model learns to predict the most likely continuation of a sequence.
Additional training that adapts a pretrained model to follow instructions, preferences, or specialized requirements.
A mechanism that assigns relevance weights to tokens so the model can use important contextual relationships.
The maximum amount of tokenized information a model can consider at one time.
A fine-tuning approach that uses human preferences to train a reward model and improve generated responses.
The process of providing input to a trained model and receiving its generated output.
An architecture that routes each input to selected specialized subnetworks, reducing the computation required per input.
The reduction of numerical precision in model weights to decrease memory and computational requirements.
The design of inputs that guide an LLM toward a desired format, behavior, or task outcome.
An instance in which an LLM generates information that is incorrect, unsupported, or fabricated.
A model capable of processing or generating multiple data types, such as text, images, audio, or 3D content.
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