Glossary Library

Technical GlossaryNatural Language Processing

Factual Consistency Evaluation

An evaluation dimension that measures how consistent a generated summary or answer is with the facts in the source content.

Factual consistency evaluation shows that fluency alone is not enough in generative NLP systems. A summary or answer may be linguistically strong, but if it introduces unsupported information it is not reliable. For that reason, accuracy verification should be treated as a separate quality layer, especially in summarization, RAG, and QA systems.

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Explore these concepts to continue your artificial intelligence journey.

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Perplexity

A measure, especially common in language models, that summarizes how surprised a probability model is by the data.

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Train / Validation / Test Split

A core data splitting approach used to separate model learning, tuning, and final evaluation in an honest way.

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K-Fold Cross Validation

A method that repeatedly evaluates a model across different data folds to provide a more reliable estimate of performance.