What is BLEU primarily used for?

BLEU, which stands for Bilingual Evaluation Understudy, is primarily used for assessing the quality of machine-translated text. It is a metric that compares the similarity of a candidate translation (the output from a machine translation system) to one or more reference translations (human-generated translations). BLEU calculates how many n-grams (contiguous sequences of words) in the candidate text appear in the reference texts, providing a quantitative measure of translation quality.

The strength of BLEU lies in its ability to effectively focus on precision and recall aspects of translation, which helps to indicate how closely the machine-generated output matches human expectations in terms of fluency and fidelity. Therefore, it provides a standardized way to evaluate and compare the performance of different machine translation systems, allowing researchers and practitioners to benchmark their solutions against established standards in natural language processing.

Other options, such as evaluating the accuracy of graphical data or comparing different AI models, are not relevant to BLEU, as the metric specifically addresses textual similarity in the context of translation. Similarly, while BLEU might indirectly contribute to improving generative text output by revealing weaknesses in a model's translations, its primary purpose is not focused on generation improvement but on evaluation.