In this tutorial, we will introduce four popular evaluation metrics for NLP model (Natural Language Processing model): ROUGE, BLEU, METEOR, and BERTScore.
Evaluating LLMs is critical to understand their capabilities and limitations across different tasks.
As LLMs continue to advance rapidly, robust evaluation metrics are needed to benchmark and compare different models.
Without proper evaluation, it would be difficult to determine which models work best for specific use cases.
However, evaluating LLMs poses unique challenges compared to other NLP models:
- LLMs exhibit some randomness and creativity in their outputs based on prompt engineering. This makes consistent evaluation difficult.
- LLMs are computationally expensive, so evaluation metrics must be efficient.
- Assessing qualities like coherence, factual accuracy, and bias requires going beyond simple word-matching metrics.
The guide covers the most widely used metrics for evaluating NLP models:
- BLEU — Measures precision of word n-grams between generated and reference texts.
- ROUGE — Measures recall of word n-grams and longest common sequences.
- METEOR — Incorporates recall, precision, and additional semantic matching based on stems and paraphrasing.
- BERTScore — Matches words/phrases using BERT contextual embeddings and provides token-level granularity.
These metrics each have strengths and limitations. Using them together provides a more comprehensive evaluation:
- BLEU and ROUGE are simple and fast but rely only on word matching.
- METEOR captures some semantic similarity.
- BERTScore incorporates…
