BLEU Score: Definition and Examples

The BLEU Score is one of the most widely used metrics in natural language processing (NLP) for evaluating the quality of texts produced by machine translation or text generation systems. Developed by Kishore Papineni and his team at IBM in 2002, it measures the degree of overlap between a candidate text (generated by the machine) and one or more reference texts (produced by humans).

Specifically, the BLEU Score works by comparing sequences of words (called n-grams) between the generated text and the references. It calculates the precision of n-grams: how many groups of 1 word (unigrams), 2 words (bigrams), 3 words (trigrams), and 4 words (4-grams) from the candidate text appear in the references. The final score combines these precisions with a brevity penalty that penalizes texts that are too short compared to the references.

The score ranges from 0 to 1 (often expressed as a percentage from 0 to 100). A score of 1 means a perfect match with the reference, which is extremely rare in practice. In machine translation, a BLEU Score above 30 is generally considered acceptable, and above 50 as very good. It is important to note that the BLEU Score mainly measures lexical fidelity and does not necessarily capture fluency, meaning, or stylistic quality.

In the context of prompt engineering, understanding the BLEU Score allows for objective evaluation of whether a language model's responses match expected outputs. This is particularly useful when iterating on prompts for translation, summarization, or paraphrasing tasks, as it provides a numerical indicator to compare different prompt versions.