BLEU/ROUGE Scores

What Are BLEU and ROUGE Scores?

BLEU and ROUGE scores are established metrics in natural language processing (NLP) for evaluating similarity between machine-generated text and human-authored reference text. These metrics quantify overlap at word and phrase level, enabling systematic comparison and quality tracking for generative AI systems.

BLEU (Bilingual Evaluation Understudy)

• Assesses precision of n-gram overlap between candidate and reference texts
• Originally designed for machine translation
• Standard for language generation, image captioning, technical documentation

ROUGE (Recall-Oriented Understudy for Gisting Evaluation)

• Suite of metrics focusing on recall
• Measures n-gram, word sequence, and word pair overlaps
• Particularly influential in text summarization, paraphrase generation, question answering

Both are reference-based metrics requiring one or more ground-truth texts for comparison.

Theoretical Foundation

BLEU: Precision-Oriented

Formula:

BLEU = BP × exp(Σ wₙ log pₙ)

Where:

• BP = Brevity Penalty (discourages short outputs)
• wₙ = Weight for n-gram precision
• pₙ = Modified precision for n-grams of size n

Modified Precision:

pₙ = (Matching n-grams of size n) / (Total n-grams of size n in candidate)

Brevity Penalty:

BP = {1 if c > r; e^(1-r/c) if c ≤ r}

where c = candidate length, r = reference length

ROUGE: Recall-Oriented

Key Variants

• ROUGE-N: N-gram overlap (ROUGE-1 for unigrams, ROUGE-2 for bigrams)
• ROUGE-L: Based on Longest Common Subsequence
• ROUGE-W: Weighted LCS (higher scores for longer matches)
• ROUGE-S: Skip-bigrams (word pairs in order, possibly with gaps)

ROUGE-N Formula:

ROUGE-N = (Overlapping n-grams) / (Total n-grams in reference)

ROUGE-L Formula:

Precision = LCS(X,Y) / |Y|
Recall = LCS(X,Y) / |X|
F1 = (1+β²)PR / (R + β²P)

How They Are Used

BLEU Workflow

1. Tokenize candidate and reference texts
2. Extract n-grams (typically up to 4-grams)
3. Count matching n-grams with clipping to avoid over-counting
4. Calculate modified precision for each n-gram order
5. Compute geometric mean of precisions
6. Apply brevity penalty
7. Output score between 0 and 1

ROUGE Workflow

1. Tokenize and normalize both texts
2. Count overlapping n-grams or find LCS
3. Calculate recall, precision, F1
4. For multiple references, take maximum or average
5. Output score between 0 and 1

Practical Computation

BLEU (Python, NLTK)

from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction

reference = [['the', 'cat', 'is', 'on', 'the', 'mat']]
candidate = ['the', 'cat', 'is', 'on', 'mat']

bleu_score = sentence_bleu(reference, candidate, 
                          smoothing_function=SmoothingFunction().method1)

ROUGE (Python, rouge-score)

from rouge_score import rouge_scorer

reference = "the cat is on the mat"
candidate = "the cat is on mat"

scorer = rouge_scorer.RougeScorer(['rouge1', 'rougeL'], use_stemmer=True)
scores = scorer.score(reference, candidate)

Use Cases

Machine Translation

• BLEU is standard for comparing translation outputs

Text Summarization

• ROUGE is default metric for evaluating summary coverage

Image Captioning & Dialogue

• Both metrics applied where responses should match references

Question Answering

• Benchmark overlap with ground truth in RAG and closed-domain QA

Automation in QA Pipelines

• Automatically compute scores for AI-generated annotations
• Set thresholds for acceptance/flagging

Metric Variants

BLEU Variants

• BLEU-1: Unigram precision only
• BLEU-2: Adds bigrams
• BLEU-4: Up to 4-grams (standard for translation)

ROUGE Variants

• ROUGE-1: Unigram overlap
• ROUGE-2: Bigram overlap
• ROUGE-L: Longest common subsequence
• ROUGE-S: Skip-bigram overlap

Interpreting Scores

Notes:

• Multiple references improve reliability
• BLEU is stricter; high scores are rare for creative outputs
• ROUGE is more tolerant to paraphrasing

Strengths and Limitations

Common Pitfalls

• Both underrate outputs using synonyms or paraphrasing
• BLEU penalizes short outputs
• Low scores for valid out-of-reference answers
• Score interpretation requires dataset context

Comparison Table

Best Practices

• Use BLEU-4 for translation and strict sequence fidelity
• Use ROUGE-L and ROUGE-1 for summarization
• Multiple references increase fairness
• Combine with METEOR, BERTScore, human evaluation
• Integrate into CI/CD for scalable evaluation

Practical Tips

• Normalize text: lowercase, remove punctuation
• Use smoothing for short outputs in BLEU
• Determine “good” scores empirically per task
• Default to paraphrasing—quotes should be rare exceptions (15+ words)
• One quote per source maximum

Frequently Asked Questions

What’s the difference between BLEU and ROUGE? BLEU measures precision (how much of candidate is in reference); ROUGE measures recall (how much of reference is in candidate).

When should I use BLEU vs ROUGE? Use BLEU for translation and precision-critical tasks; use ROUGE for summarization and coverage-critical tasks.

Are high scores always better? Not necessarily; scores must be interpreted in context of task, dataset, and comparison with baselines.

Can these metrics evaluate semantic similarity? No; they measure lexical overlap, not semantic meaning. Combine with embedding-based metrics for semantic evaluation.

References

• GeeksforGeeks: Understanding BLEU and ROUGE Score
• SuperAnnotate: BLEU - ROUGE Guide
• Elastic: Evaluating RAG Metrics
• BLEU Score - Wikipedia
• NLTK BLEU Documentation
• rouge-score PyPI