AI Reference 056 Large Language Models Wikipedia guided Primary sources linked

AI Hallucinations

Definition

A hallucination is generated content that is false, unsupported or inconsistent with the evidence available to the system. Language generation optimizes likely sequences, not guaranteed factual truth, so high-stakes factual work needs grounding and verification.

What is an AI hallucination?

In generative AI, a hallucination is output that is false, unsupported by the source material or inconsistent with reality. Examples include invented citations, incorrect dates, fabricated product features or confident statements about events that never happened.

Why hallucinations occur

LLMs are trained to model plausible token sequences. Plausibility and truth often overlap, but they are not the same objective. If the model lacks reliable evidence, it can still generate a fluent continuation that resembles a factual answer.

How systems reduce the problem

Retrieval, tool use, explicit source constraints, verification steps and domain-specific evaluation can reduce unsupported output. None creates an absolute guarantee. High-stakes systems need a process for checking claims rather than relying on the tone or confidence of generated text.

Related terms, defined

Large Language Models Retrieval-Augmented Generation Retrieval-augmented generation combines a generative model with retrieval from an external collection such as documents, databases or search results. Retrieved material is inserted into the model's context so the answer can be grounded in information outside the model's parameters. Large Language Models Function Calling and Tool Use Function calling and tool use let a model request structured actions such as querying a database, calling an API or using search, with the surrounding application executing the action. Large Language Models AI Agents An AI agent is a system in which a model can select or plan actions over multiple steps, usually with tools, memory and feedback from an environment. The language model is only one part of the agent; orchestration, permissions, tools and stopping conditions are equally important. Large Language Models Mixture-of-Experts Models A mixture-of-experts model contains multiple expert subnetworks and routes each token or input through only a subset of them. This can increase total parameter capacity without activating every parameter on every forward pass. Large Language Models What Is a Large Language Model? A large language model is a statistical model trained on large collections of text or multimodal data to predict and generate token sequences. Modern LLMs are usually transformer-based and are adapted to conversation, coding, tool use and other tasks through post-training and application-level systems.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.

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