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LLMs are trained on large text datasets to model language and generate context-appropriate text, often starting as next-token predictors and later fine-tuned for instruction following.
Large Language Models (LLMs) are AI models—typically transformer-based—trained on vast text corpora to perform natural language processing tasks, especially language generation. Their core capability is predicting and producing text in context, enabling them to generate, summarize, translate, and analyze language. In practice, LLMs are often trained first as next-token predictors (e.g., GPT-style) and then adapted via fine-tuning (such as instruction tuning and methods like RLHF) to follow user instructions and behave more like assistants. LLMs’ capabilities extend beyond basic text generation through architectural and training choices (e.g., attention mechanisms and context windows, and sometimes mixture-of-experts for efficiency). They can also be extended using external tools and data sources via techniques such as retrieval-augmented generation, tool use, and prompting strategies (prompt engineering). Because LLMs can be sensitive to training data quality and can produce fluent but incorrect outputs (hallucinations), evaluation and safety measures—using benchmarks, perplexity-based metrics, and adversarial tests—are important for assessing reasoning, factual accuracy, alignment, and safety.
LLMs are trained on large text datasets to model language and generate context-appropriate text, often starting as next-token predictors and later fine-tuned for instruction following.
Transformer attention and context windows determine how LLMs relate tokens across sequences; variants like mixture-of-experts can improve inference efficiency.
LLM capabilities can be extended via prompting and external mechanisms such as retrieval-augmented generation and tool use, but reliability issues like hallucinations require evaluation and safety techniques.
A training objective where the model learns to predict the next token in a sequence given prior context.
A neural network design that uses attention to model relationships between tokens across an input sequence.
A component that computes how much each token should focus on other tokens in the context when producing representations.
The maximum amount of preceding tokens an LLM can consider when generating or predicting new tokens.
Post-pretraining training that adapts an LLM to specific behaviors, such as instruction following or assistant-like responses.
A method that uses human preferences to train a reward model and then fine-tunes the LLM to better match those preferences.
An architecture with multiple specialized subnetworks (“experts”) where a gating mechanism routes inputs to the most relevant expert(s).
Techniques for crafting inputs (prompts) to elicit desired behaviors and formats from an LLM.
A method that combines LLM generation with retrieved external information to improve factuality and relevance.
Fluent but incorrect or unsupported model outputs that appear plausible while being factually wrong or unfaithful to provided sources.
A standard metric for language models that measures how well the model predicts a dataset, based on token likelihoods.
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