What is BLEU score?

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The BLEU (Bilingual Evaluation Understudy) score is a widely used metric for evaluating the quality of text generated by models, especially in machine translation and other natural language generation tasks. It measures how closely a generated text (candidate) matches one or more reference texts written by humans.

Here’s a detailed explanation:

1. How BLEU Works

• BLEU compares n-grams (sequences of 1, 2, 3, … words) in the generated text with those in the reference text.

• It calculates precision, which is the fraction of n-grams in the generated text that also appear in the reference.

• To avoid rewarding very short outputs, BLEU applies a brevity penalty, ensuring that the generated text is not unnaturally short compared to the reference.

• The final BLEU score is usually between 0 and 1, often reported as a percentage (0–100%). Higher scores indicate closer similarity to the reference text.

2. Key Points

• n-gram matching: BLEU considers multiple n-grams (e.g., unigram, bigram, trigram) and combines their scores, often using a geometric mean.

• Multiple references: BLEU can handle multiple reference texts, which improves evaluation reliability.

• Automatic evaluation: Unlike human evaluation, BLEU is fast and objective, making it suitable for large-scale testing.

3. Limitations

• BLEU focuses only on surface similarity, so it may penalize valid paraphrases that convey the same meaning but use different wording.

• It does not measure fluency or semantic correctness beyond n-gram overlap.

• Often complemented by other metrics like ROUGE, METEOR, or human evaluation for a more complete assessment.

In short: BLEU is an automatic metric to quantify how closely a machine-generated text resembles reference human text, using n-gram matching and a brevity penalty. It is widely used in translation, summarization, and other text generation tasks but has limitations in capturing meaning beyond literal word overlap.

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