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LLMs are trained on vast text corpora to perform language tasks, especially generation, and power many chatbot systems.
A large language model (LLM) is an AI model—typically a neural network—trained on large amounts of text to perform natural language processing tasks, especially language generation. LLMs can generate, summarize, translate, and analyze text across many contexts, and they are a foundational technology behind modern chatbots. Their outputs can be affected by biased or inaccurate training data, so reliability and safety are important considerations. LLMs’ core capabilities come from how they are trained and architected. They are commonly based on transformer architectures with attention mechanisms that let the model relate tokens across a context window, enabling effective next-token prediction (autoregressive) or missing-token prediction (masked models). Training usually involves pretraining (often next-word prediction) followed by post-training such as fine-tuning and instruction-following methods (e.g., RLHF), and models may also use extensions like mixture-of-experts to reduce inference cost. LLMs are evaluated using measures such as perplexity and task benchmarks that test reasoning, factual accuracy, alignment, and safety.
LLMs are trained on vast text corpora to perform language tasks, especially generation, and power many chatbot systems.
Core architecture is often transformer-based with attention, supporting next-token prediction within a context window.
Training typically includes pretraining plus fine-tuning/post-training (e.g., instruction tuning and RLHF) to improve helpfulness and alignment.
LLM performance is assessed using metrics like perplexity and benchmarks covering reasoning, accuracy, bias, and safety.
An AI model trained on large text datasets to perform natural language processing tasks, especially generating and transforming text.
A neural network design that uses attention mechanisms to model relationships between tokens in a sequence.
A method that computes how relevant different tokens are to each token’s representation, enabling context-aware predictions.
The span of tokens the model can consider when generating or predicting the next output.
A training objective where the model learns to predict the next token in a sequence given prior tokens.
Additional training after pretraining to shape an LLM’s behavior, often to follow instructions or improve alignment.
A post-training approach that uses human preferences to train a reward model and then optimize the LLM to better satisfy that reward.
An architecture where multiple specialized subnetworks (“experts”) are routed to for each input to reduce computation while maintaining quality.
A standard language-model evaluation metric measuring how well a model predicts a dataset, related to the average negative log-likelihood per token.
Fluent but factually incorrect or unfaithful text generated by an LLM that does not match the provided information or ground truth.
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