AI glossary
Natural Language Generation (NLG)
Natural language generation (NLG) is the subfield of AI and natural language processing concerned with producing human-readable text. This output can come from structured data, an internal representation, or, in the case of modern language models, directly as a continuation of preceding text.
NLG vs NLU
The NLG meaning is best understood by contrasting it with natural language understanding (NLU). While NLG focuses on the “output” side, producing language from data or internal states, NLU handles the “input” side, interpreting and extracting meaning from human language.
Think of NLU as the system reading and comprehending your query, and NLG as the system formulating the response. Although these tasks are distinct, modern systems often perform both simultaneously within a single architecture.
Traditional (template and data-driven) NLG
Before the rise of large language models, NLG systems relied heavily on structured inputs. These early approaches typically worked by filling structured data into predefined templates or by using rule-based grammars to turn that data into sentences. This method was well suited for repetitive, data-heavy writing where the structure of the output was predictable.
A well-documented real example is the Associated Press’s use of automated NLG software, built by Automated Insights using its Wordsmith platform, starting in 2014. The system generated large volumes of corporate earnings reports directly from financial data. This automation freed journalists to focus on more complex stories rather than routine reporting.
Automated weather reports represent another long-standing use case for this approach. These systems generate routine forecast text directly from structured weather data, ensuring consistency and speed for daily updates that follow a strict format.
How modern LLMs do NLG
Modern large language models perform NLG end-to-end. Instead of filling a template, the model generates text one token at a time. It predicts each next token based on everything that came before it. This autoregressive approach allows the model to produce far more varied and context-sensitive text than template-based systems.
By leveraging architectures like the Transformer, these models capture complex linguistic patterns and dependencies that rigid templates cannot handle. The process of converting input text into these discrete units is handled via tokens, which serve as the fundamental building blocks for generation.
Real-world examples
Chatbots and AI writing assistants built on large language models are a direct, everyday application of NLG AI. These tools generate original replies rather than selecting from a fixed set of template sentences. This capability allows for dynamic conversations that adapt to the user’s specific context and tone.
When you interact with a chatbot, the system is actively using NLG to construct a coherent response in real-time. Similarly, AI writing assistants help draft emails, articles, and code snippets by predicting the most likely continuation of your prompt based on vast training data.
Evaluating generated text
Generated text is commonly evaluated on qualities like fluency, coherence, and factual accuracy. Fluency asks whether the text reads naturally, while coherence checks if the content stays logically consistent throughout. Factual accuracy ensures the output correctly reflects the source data or established facts it is meant to describe.
A critical distinction in evaluation is that a fluent sentence is not automatically a factually correct one. Modern models can produce grammatically perfect text that contains hallucinations or errors. Therefore, assessing the reliability of the content is just as important as judging its readability.
FAQ
Is NLG the same as NLP?
No. Natural Language Processing (NLP) is the broader field encompassing both understanding (NLU) and generation (NLG). NLG is a specific subfield focused on creating text.
Do large language models use templates for NLG?
Generally, no. Modern LLMs generate text token by token, allowing for high variability and context sensitivity, unlike traditional template-based systems.