📏 Relevant Metrics to Assess Foundation Model Performance
Evaluating the output of a foundation model requires selecting the right metrics based on the task type (e.g., summarization, translation, classification). These metrics help compare outputs against reference answers and measure quality, relevance, and fluency.
🧾 1. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
🔍 What It Measures:
- Overlap between generated text and reference summaries
- Focuses on recall of words, sequences, or n-grams
🧪 Best For:
- Text summarization, content compression, document distillation
🧠 Variants:
- ROUGE-1: Unigram overlap
- ROUGE-2: Bigram overlap
- ROUGE-L: Longest common subsequence
🌐 2. BLEU (Bilingual Evaluation Understudy)
🔍 What It Measures:
- Overlap of n-grams between generated and reference text, using precision
🧪 Best For:
- Machine translation, short-form generation, paraphrasing
🧠 Notes:
- Scores from 0 to 1 (or 0 to 100%)
- Higher = better alignment with expected reference
🧠 3. BERTScore
🔍 What It Measures:
- Semantic similarity using pre-trained BERT embeddings
- Goes beyond surface word overlap
🧪 Best For:
- Natural language generation, paraphrasing, and semantic comparison
🧠 Benefit:
- Captures meaning even if words differ (e.g., synonyms)
📊 4. Accuracy
🔍 What It Measures:
- Percentage of correct predictions vs. total predictions
🧪 Best For:
- Classification tasks (e.g., spam detection, intent classification)
🎯 5. F1 Score
🔍 What It Measures:
- Harmonic mean of precision and recall
🧪 Best For:
- Imbalanced datasets
- Ensures both false positives and false negatives are considered
📉 6. Perplexity
🔍 What It Measures:
- How well a language model predicts the next word/token
- Lower = better
🧪 Best For:
- Evaluating fluency of language models and text generation tasks
📦 7. NDCG (Normalized Discounted Cumulative Gain)
🔍 What It Measures:
- Ranking relevance in retrieval-based systems
- Prioritizes high-relevance items at the top of the result list
🧪 Best For:
- Search, RAG, recommendation systems
🧩 Summary Table
| Metric | Measures | Best For |
|---|---|---|
| ROUGE | Recall-based n-gram overlap | Summarization |
| BLEU | Precision-based n-gram overlap | Translation, short-form text |
| BERTScore | Semantic similarity | Paraphrasing, QA, summarization |
| Accuracy | Correct vs. incorrect predictions | Classification |
| F1 Score | Balance of precision and recall | Imbalanced classification |
| Perplexity | Next-token prediction quality | Language modeling |
| NDCG | Ranking quality in search | RAG, vector search |
Selecting the right evaluation metric ensures that your foundation model meets the performance standards for accuracy, relevance, fluency, and utility in real-world tasks.