How does Reality Defender detect generative text?
Reality Defender’s text detector identifies AI-generated text by learning from large sets of paired human-written and model-generated examples. The detector processes the entire text as a whole and maps it into a high-dimensional vector space, enabling it to identify subtle statistical patterns that correlate with LLM-generated content.
This approach allows the model to detect signals that humans typically can’t perceive, such as distributional patterns, phrasing consistency, and token-level likelihood signatures, and resulting in highly accurate and robust predictions.
What LLMs are supported by Reality Defender’s Text Detection?
Text detection is platform-agnostic, meaning it supports virtually all LLMs that produce English-language text.
This includes:
Closed-source models: ChatGPT, Claude, Gemini, Perplexity models, etc.
Open-source models: LLaMA variants, Mistral, Mixtral, Falcon, etc.
Consumer tool outputs: Bard, Bing Copilot, GitHub Copilot text, character/roleplay generators.
Obscure or lesser-known LLMs: In most cases, detection still works due to broad generalization capabilities.
If an LLM can produce readable English text, RD can likely detect it.
How is AI-generated text created?
Large Language Models (LLMs) generate text one token at a time by predicting the next most likely word or character based on massive training datasets. This process enables them to:
Learn grammar, structure, and writing style
Memorize or generalize facts
Produce fluent, human-like prose
Adapt tone and format
Because the generation process relies on statistical prediction, these patterns, even when extremely subtle, can be distinguishable from human writing.