Explore RAG evaluation metrics like BLEU score, ROUGE score, PPL, BARTScore, and more. Discover how Elastic is evaluating RAG with UniEval.

N-gram metrics

BLEU score

ROUGE score

METEOR score

Intrinsic metrics

Perplexity (PPL)

Model-based metrics

BERTScore

BLEURT

BARTScore

UniEval: Elastic’s choice for evaluating RAG

Real-world usage of UniEval

Conclusion

There are various metrics used to evaluate RAG, such as: N-gram metrics (including BLEU score, ROUGE score & METEOR score), Intrinsic metrics (like PPL), Model-based metrics (such as BERTScore, BLEURT and BARTScore), and Elastic's choice: UniEval.

What metrics are commonly used to evaluate RAG?

UniEval evaluates RAG by unifying all evaluation dimensions into a Boolean Question Answering framework, allowing a single model to assess a generated text from various angles.

How does UniEval evaluate RAG?

RAG evaluation metrics: UniEval, BLEU, ROUGE & more

RAG evaluation metrics: A journey through metrics

Learn how scalar quantization can be used to reduce the memory footprint of vector embeddings in Elasticsearch through an experiment.

Understanding scalar quantization in Elasticsearch

Experimentation: Evaluating scalar quantization

Overview of methodology

Results

The benefits of using scalar quantization in Elasticsearch include reducing the memory footprint of vector embeddings without significantly affecting retrieval performance.

What are the benefits of using scalar quantization in Elasticsearch?

Evaluating scalar quantization in Elasticsearch

Author

Thanos Papaoikonomou

Articles

RAG evaluation metrics: A journey through metrics

Evaluating scalar quantization in Elasticsearch