RAG Models Evaluation | Top 12 Metrics for Retrieval Augmented Generation

Описание: RAG Models Evaluation Top 12 Metrics for Retrieval-Augmented Generation
At its core, a RAG system works, in two main steps:
Retrieval: The system fetches, relevant information from a document store, vector database, or external API based on the user’s query.
Generation: The language model, uses that retrieved information, to generate a coherent, contextually grounded answer.
So, instead of relying solely, on its pre-trained knowledge, the RAG system, can augment its responses, with up-to-date, and domain-specific data.
RAG metrics, can be divided into retrieval part metrics, generator part metrics, and end to end metrics.
1. MRR ( or Mean Reciprocal Rank)
MRR measures, how high the correct answer, appears in the ranked list, of retrieved documents.
2. nDCG (Normalized Discounted Cumulative Gain)
nDCG measures, how relevant all the retrieved documents are, but gives more credit to, documents ranked higher than lower ones.

3. Precision@K
Precision@K checks, how many of, the top K retrieved documents, are relevant.
4. Recall@K

Recall@K measures, whether the system was, able to retrieve all possible, relevant documents, within the top K.
Now, let us discuss, the 5 generation-side metrics.
5. BLEU stand for (Bilingual Evaluation Understudy)
BLEU measures, how similar the generated answer is, to a reference answer, using exact word overlaps (or n-grams).
6. ROUGE stands for (Recall-Oriented, Understudy, for Gisting Evaluation)

ROUGE, measures how much, of the important content, from the reference answer, appears in the generated answer.

7. METEOR stand for (Metric for Evaluation, of Translation, with Explicit Ordering)

METEOR measures similarity, but is more flexible than, BLEU, because it considers synonyms, word stems, and paraphrasing.

8. Perplexity

Perplexity measures, how well a language model predicts, the next word, where lower perplexity means, better fluency and confidence.

9. BERTScore

BERTScore, uses embeddings (and semantic similarity), rather than word overlap, making it robust to paraphrasing.

Now, its time to discuss 4, end-to-end, RAG evaluation metrics.

10. Groundedness

Groundedness measures, how much of the generated answer, is backed by the, retrieved documents.

11. Faithfulness

Faithfulness evaluates, whether the answer accurately represents, the source information, without twisting, misinterpreting, or adding unsupported claims.

12. Answer Relevance

Answer relevance measures, how well the generated answer, directly addresses the, user’s question.

13. Hallucination Rate

Hallucination rate measures, the percentage of claims, in the answer, that are not supported by, retrieved documents or contradict evidence.

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