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Interactive 3D/BLEU and ROUGE Metrics for Text Evaluation
Reference
the cat sat on the mat
Hypothesis
a cat is sitting on the mat
1-gram match
2-gram match
3-gram match
4-gram match
Highlighting: 2-grams
BLEU Score
0.001
1-gram P
0.571
2-gram P
0.333
3-gram P
0.200
4-gram P
0.000
Brevity Penalty (BP): 1.000
ROUGE Scores
ROUGE-1
0.615
Unigram F1
ROUGE-2
0.364
Bigram F1
ROUGE-L
0.615
Longest Common Subsequence F1
Controls
Example Pairs
N-gram Highlight
Metric View
BLEU measures n-gram precision with a brevity penalty. ROUGE measures recall-oriented overlap. Highlighted tokens show matching n-grams. Edit either sentence to see scores update live. Try the "Too Short" preset to see brevity penalty collapse BLEU.

BLEU and ROUGE Metrics for Text Evaluation - Interactive Visualization

BLEU (Bilingual Evaluation Understudy) measures n-gram precision between a hypothesis and reference text, with a brevity penalty for short outputs. ROUGE measures recall-oriented overlap and is preferred for summarization tasks. ROUGE-L uses longest common subsequence to capture sentence-level structure. Both metrics have known weaknesses - they correlate poorly with human judgments on open-ended generation tasks.

  • BLEU is geometric-mean n-gram precision × brevity penalty - one very short output collapses the score
  • ROUGE-1 and ROUGE-2 measure unigram and bigram F1; ROUGE-L captures sentence-level word order
  • Highlighted tokens show exactly which n-grams match between reference and hypothesis
  • Neither BLEU nor ROUGE correlates well with human quality judgments for chat or long-form generation

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.