BLEU Score

Why It Matters

BLEU score is the most widely used automatic metric for text generation evaluation. When papers report “our model achieves 35.5 BLEU on WMT translation,” they’re using this standard benchmark. Understanding BLEU is essential for reading ML research and evaluating generation systems.

The metric counts n-gram (word sequence) matches between generated text and reference texts. If your model outputs “The cat sat on the mat” and the reference is “The cat sat on a mat,” BLEU rewards the matching phrases while penalizing the difference.

For AI engineers, BLEU provides quick automated evaluation during development. You can run BLEU on thousands of examples in seconds, making it useful for iteration. But it’s a proxy metric. High BLEU doesn’t guarantee good output, and low BLEU doesn’t mean bad output. Use it as a directional signal, not a final judgment.

Implementation Basics

How BLEU Works

1. Count matching n-grams (1-grams, 2-grams, 3-grams, 4-grams) between output and references
2. Calculate precision for each n-gram size
3. Apply brevity penalty if output is shorter than reference (prevents gaming with short outputs)
4. Combine scores using geometric mean

Score Interpretation

• BLEU ranges from 0 to 1 (often reported as 0-100)
• 0.4+ (40+) is generally considered good for machine translation
• Scores vary wildly by task, so don’t compare BLEU across different tasks
• Multiple references improve reliability (more ways to be “correct”)

Implementation Libraries like sacrebleu, nltk, and evaluate provide BLEU implementations. Use sacrebleu for reproducible scores, as it standardizes tokenization which affects results.

Limitations BLEU only measures surface-level overlap. It misses:

• Semantic similarity (paraphrases score low)
• Fluency and coherence
• Factual correctness

Two outputs with identical meaning but different wording will have different BLEU scores. Combine BLEU with human evaluation for complete assessment.