Description: Factual consistency refers to the degree to which text generated by a natural language processing (NLP) model aligns with known and verifiable facts. This concept is fundamental in evaluating the quality of NLP models, as text lacking factual consistency can lead to misinformation and the spread of errors. Factual consistency implies that the claims made in the text must be coherent with reality, including historical, scientific, and cultural data. A model’s ability to maintain this consistency is crucial, especially in applications where information accuracy is vital, such as content generation, summarization, or virtual assistance. Factual consistency evaluation is often performed through comparisons with knowledge databases, fact-checkers, and other reliable information sources. In summary, factual consistency is a key indicator of the reliability and usefulness of NLP systems, as it ensures that the information provided is correct and relevant to users.
