LLM Evaluation: 15 Metrics You Need to Know

Below is a comprehensive blog on the Top 15 Evaluation Metrics for Large Language Models (LLMs). We’ll begin by looking at why evaluating LLMs is crucial, then discuss different ways metrics are commonly divided, and finally walk through each of the 15 metrics in detail. 

Why Evaluate Large Language Models?

Large Language Models (LLMs) have become ubiquitous, powering everything from conversational agents to summarization systems. However, not all generated text is equal—some outputs may be more fluent, factually correct, diverse, or semantically coherent than others. Metrics are therefore critical to:

• Compare models: Decide which model is best for a given use case.
• Track progress: Measure improvements during model training or fine-tuning.
• Guide development: Pinpoint weaknesses (e.g., factual errors, repetitiveness).

But evaluating LLMs is tricky. Language is multi-dimensional—it involves correctness, coherence, style, factuality, diversity, and more. No single metric captures all these aspects, so evaluation often involves multiple metrics plus human judgment. 

For tasks involving classification, evaluation metrics for classification play a crucial role in assessing model performance. These metrics help ensure the reliability and accuracy of predictions, making them essential for LLM-powered applications.

How Are These Metrics Divided?

Broadly, you can group LLM evaluation metrics into the following categories:

1. Language Modeling Metrics (Statistical)
   
   • These measure how well a model predicts text, typically at the token or sequence level.
   • Example: Perplexity (PPL).
2. Lexical Overlap Metrics
   
   • Compare n-grams or word overlaps between a generated text and a reference.
   • Examples: BLEU, ROUGE, METEOR, CIDEr.
3. Embedding-Based Metrics
   
   • Leverage semantic embeddings (e.g., from Transformers) to measure similarity rather than exact overlap.
   • Examples: BERTScore, MoverScore.
4. Learned Metrics
   
   • Neural models fine-tuned on human judgments to directly predict quality scores.
   • Examples: BLEURT, COMET.
5. Diversity Metrics
   
   • Track repetitiveness or variety in generated text.
   • Examples: Distinct-n, Self-BLEU.
6. Task/Domain-Specific or Structural Metrics
   
   • Designed to capture factual consistency or semantic relations.
   • Examples: Q², SPICE.
7. Human Evaluation
   
   • The “gold standard” for evaluating coherence, style, correctness, and more nuanced aspects.

Understanding the Top 15 Metrics for LLM Evaluation

1. Perplexity

Perplexity (PPL) is one of the oldest and most widely used statistical measures for language modeling. It gauges how well a model predicts or “perceives” the distribution of words in a given text.

1. How it’s calculated

2. Why it’s important

• A lower perplexity indicates that the model’s predictions better align with real text distributions.

3. What it helps measure

• Language fluency and the model’s ability to accurately predict sequences of words.‍

4. Limitations

• Doesn’t always correlate with downstream quality or factual correctness.
• A model can have low perplexity yet generate repetitive or contextually irrelevant text.

2. BLEU (Bilingual Evaluation Understudy)

BLEU was pioneered in the early 2000s for machine translation tasks. It has since become a baseline for many text generation tasks, thanks to its simplicity and general availability.

1. How it’s calculated
BLEU calculates n-gram precision between the generated text and reference(s). The overall score combines these precisions (commonly n=1 to 4) with a brevity penalty to discourage excessively short outputs:

2. Why it’s important

• Historically dominant in machine translation and many NLP benchmarks.
• Provides a quick, standardized measure of lexical overlap.

3. What it helps measure

• Lexical alignment: How many of the same n-grams appear in the generated output versus references.
  

4. Limitations

• Penalizes paraphrasing or synonym use that doesn’t match the reference exactly.
• Overly surface-level; can ignore semantic correctness.

3. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)

ROUGE is the go-to metric for summarization. It emphasizes recall—how much of the reference text appears in the generated summary.

1. How it’s calculated
   
   • ROUGE-N: Measures n-gram overlap.
   • ROUGE-L: Measures the Longest Common Subsequence (LCS).
   • Other variants exist (e.g., ROUGE-W for weighted LCS).
2. Why it’s important
   
   • Quick and easy to compute.
   • Has dominated summarization research for years.
3. What it helps measure
   
   • The coverage of key elements from the reference (important for summarization).
4. Limitations
   
   • Can undervalue concise or creative summaries that don’t match reference phrasing.
   • Focus on lexical overlap rather than deeper semantic accuracy.

4. METEOR (Metric for Evaluation of Translation with Explicit ORdering)

METEOR was designed to improve upon BLEU by considering synonyms, stemming, and word order. It often correlates better with human judgments than BLEU in certain contexts.

1. How it’s calculated
   
   • It uses a F-measure (precision and recall of unigrams) but also accounts for synonym and morphological matches.
   • Adds a fragmentation penalty to reflect how well word order is preserved.
2. Why it’s important
   
   • Gives partial credit for words that are similar (synonyms/stems) rather than requiring exact matches.
   • Often more fine-grained than BLEU.
3. What it helps measure
   
   • Lexical choice and word order, with some tolerance for paraphrasing.
4. Limitations
   
   • Still largely surface-level and reliant on specific resources (e.g., WordNet for synonyms).
   • May miss more complex paraphrasing structures.

5. CIDEr (Consensus-based Image Description Evaluation)

Originally proposed for image captioning, CIDEr extends n-gram matching but weights words via TF-IDF to prioritize informative words.

1. How it’s calculated
   
   • Each sentence is converted into a vector of TF-IDF weights for n-grams.
   • A cosine similarity is then computed between the candidate and reference vectors.
2. Why it’s important
   
   • Discounts common filler words and rewards more specific terms that carry content.
3. What it helps measure
   
   • The relevance and informativeness of generated text based on key content words.
4. Limitations
   
   • Heavily reference-dependent and can still penalize valid paraphrases.
   • Designed with image captioning in mind; might not generalize perfectly to all text generation scenarios.

6. BERTScore

BERTScore is an embedding-based metric that leverages pre-trained Transformer models to measure token-level similarity between reference and candidate texts.

1. How it’s calculated
   
   • Both candidate and reference tokens are converted into contextual embeddings (e.g., BERT).
   • A pairwise cosine similarity matrix is computed, and an optimal matching scheme finds precision and recall.
2. Why it’s important
   
   • Goes beyond exact word matching by capturing semantic similarity.
   • Often correlates better with human judgments than purely lexical metrics.
3. What it helps measure
   
   • Semantic alignment: how meaningfully close the candidate is to the reference.
4. Limitations
   
   • Dependent on the quality of the underlying pre-trained model.
   • Still requires a reference text; not a direct measure of factual accuracy.

7. MoverScore

MoverScore evolves from Word Mover’s Distance, using contextual embeddings to measure the “distance” between candidate and reference sentences.

1. How it’s calculated
   
   • It computes the minimum cost to “move” tokens in the candidate to match tokens in the reference, leveraging BERT (or similar) embeddings.
2. Why it’s important
   
   • Order-invariant to some extent, capturing semantic overlap without strict n-gram alignment.
   • Tends to handle paraphrases well.
3. What it helps measure
   
   • The semantic similarity between candidate and reference, factoring in embeddings rather than raw tokens.
4. Limitations
   
   • More computationally expensive than simpler metrics.
   • Reference-dependent and may still not capture deeper factual correctness.

8. BLEURT

BLEURT is a learned metric that uses a pre-trained Transformer model, further fine-tuned on human-labeled quality assessments to predict a numeric score.

1. How it’s calculated
   
   • A reference and candidate are fed into the BLEURT model, which outputs a quality score.
   • Training data often includes synthetic perturbations of sentences and human judgments.
2. Why it’s important
   
   • Incorporates human judgments directly into the training objective.
   • Can capture nuanced errors that purely lexical or embedding-based metrics may miss.
3. What it helps measure
   
   • Overall text quality and fluency, grounded in human notions of correctness.
4. Limitations
   
   • Requires robust training data for the domain of interest.
   • Performance can degrade if domain or style is very different from the training set.

9. COMET

COMET (Crosslingual Optimized Metric for Evaluation of Translation) is another learned metric specifically designed for machine translation. It’s trained on source, reference, and candidate triplets with human-annotated scores.

1. How it’s calculated
   
   • A Transformer model processes source, candidate, and reference, producing a quality score.
   • Fine-tuned on human judgments from translation data.
2. Why it’s important
   
   • Often outperforms lexical metrics in machine translation tasks.
   • Accounts for cross-lingual semantics by involving the source text.
3. What it helps measure
   
   • Translation quality in terms of fluency, adequacy, and faithfulness to the source.
4. Limitations
   
   • Primarily trained for translation tasks; might not generalize to all text generation tasks.
   • Subject to domain mismatch issues if the target text is very different from training data.

10. Distinct-n

Distinct-n was introduced to measure the diversity of generated text—important in dialogues and creative content generation where repetition is undesirable.

1. How it’s calculated
   
   • Distinct-n = (Number of unique n-grams) / (Total n-grams in the generated text).
   • Typically, Distinct-1 counts unique unigrams, Distinct-2 counts unique bigrams, and so on.
2. Why it’s important
   
   • Penalizes repetitiveness, pushing the model to generate more varied text.
   • Useful for detecting “generic” or templated responses.
3. What it helps measure
   
   • The lexical diversity of outputs, indicating if the model is repeating words/phrases excessively.
4. Limitations
   
   • A model could have high Distinct-n but still be incoherent or off-topic.
   • Doesn’t capture semantic or factual correctness.

11. F1 Score

While the F1 metric is traditionally used in classification, it can be adapted for extraction tasks (e.g., QA systems that return spans of text). It balances precision and recall.

1. How it’s calculated

2. Why it’s important

• Weighs correctness (precision) and completeness (recall) equally.
• Simple and interpretable for correct/incorrect tasks.

3. What it helps measure

• The ability of the model to retrieve or identify the right tokens or answers.
  

4. Limitations

• Assumes a binary notion of correctness.
• Doesn’t evaluate semantic nuance or partial correctness.
  

12. Self-BLEU

Self-BLEU measures the similarity among multiple generated outputs from the same model—effectively quantifying intra-model diversity.

1. How it’s calculated
   
   • For each generated sample, treat it as a “candidate” and the rest of the samples as “references,” then compute BLEU.
   • The final score is typically an average.
2. Why it’s important
   
   • Lower Self-BLEU = higher diversity.
   • Helps detect models that produce repetitive or generic outputs.
3. What it helps measure
   
   • Variety of language generation across multiple samples.
4. Limitations
   
   • Doesn’t directly evaluate correctness, coherence, or factualness.
   • If a model has many paraphrased but semantically similar outputs, Self-BLEU might still rate them as too “similar.”

13. Q² (Quality & Qualitative for Consistency)

Q² is designed for factual consistency and coherence. It uses a question generation and question answering approach to verify the correctness of generated text.

1. How it’s calculated
   
   • A separate system generates questions from the generated text.
   • Another system answers those questions based on the source or reference text.
   • The answers are compared; high consistency indicates factual alignment.
2. Why it’s important
   
   • Specifically targets the factual accuracy of summaries or outputs—crucial in high-stakes content (news, medical).
3. What it helps measure
   
   • Correctness of information, focusing on whether the text actually conveys facts consistent with a reference or source.
4. Limitations
   
   • Relies on the quality of question generation and QA models; any error there can skew results.
   • Primarily relevant to tasks with a verifiable source (e.g., summarization).

14. SPICE (Semantic Propositional Image Caption Evaluation)

SPICE was introduced for image captioning, focusing on semantic propositional content by building scene graphs for candidate and reference captions.

1. How it’s calculated
   
   • Candidate and reference texts are parsed into scene-graph tuples (objects, attributes, relations).
   • Computes an F-score over these tuples.
2. Why it’s important
   
   • Goes beyond surface-level n-grams to capture relations among entities.
   • Useful where structural or relational correctness matters.
3. What it helps measure
   
   • Semantic structure alignment—are the same objects and relations present in the candidate?
4. Limitations
   
   • Requires a robust semantic parser which can introduce errors.
   • Limited to tasks where scene-graph-like structures are meaningful.

15. Human Evaluation

Despite the proliferation of automated metrics, human evaluation remains the gold standard. Humans can judge coherence, fluency, factual correctness, style, and other subtle language features that automated metrics often miss.

1. How it’s calculated
   
   • Human annotators read or compare outputs.
   • They might score them on dimensions (fluency, coherence, factual accuracy) or rank different model outputs.
2. Why it’s important
   
   • Captures nuances no automated metric can fully capture.
   • Reflects real-world user satisfaction.
3. What it helps measure
   
   • Overall quality and usability of the text, including intangible aspects (tone, creativity, etc.).
4. Limitations
   
   • Time-consuming and can be expensive at scale.
   • Subjective; requires careful guidelines and multiple annotators to ensure reliability.

Conclusion

Evaluating LLMs is multi-faceted:

• Language modeling metrics like perplexity help gauge statistical fluency.
• Lexical overlap metrics (BLEU, ROUGE, METEOR, CIDEr) are useful for tasks with well-defined references.
• Embedding-based metrics (BERTScore, MoverScore) and learned metrics (BLEURT, COMET) step up by capturing semantics.
• Diversity metrics (Distinct-n, Self-BLEU) ensure outputs aren’t repetitive or generic.
• Factual-consistency or semantic-structure metrics (Q², SPICE) help verify correct information is conveyed.
• Human evaluation remains irreplaceable for capturing subjective qualities.

No single metric is perfect. Practitioners often combine multiple automated scores with human evaluation for a complete picture. The key is choosing metrics that align with your task (e.g., factual vs. creative tasks) and validating them through human assessments whenever possible.