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

Learn to evaluate your search system in the context of better understanding the BEIR benchmark, with tips & techniques to improve your search evaluation processes.

Understanding the BEIR benchmark in search relevance evaluation

Structure of a BEIR dataset

Leveraging the BEIR benchmark for search relevance evaluation

Main takeaways & next steps

The BEIR benchmark & Elasticsearch search relevance evaluation

Evaluating search relevance part 1 - The BEIR benchmark

Using the Phi-3 language model as a relevance judge, with tips & techniques to improve the agreement with human-generated annotation

A Short Introduction to Phi Models

A note on terminology

Our experiment

About the annotation task

About the language model

Results Summary

The role of word choice on prompt performance

Tone words

Output instructions

"Take a step back" reasoning

Role playing

More precise instructions

Pairwise

Few-shot chain-of-thought

Ensemble

Microsoft Phi-3 model for evaluating search relevance

Evaluating search relevance part 2 - Phi-3 as relevance judge

Author

Thanos Papaoikonomou

Articles

RAG evaluation metrics: A journey through metrics

Evaluating scalar quantization in Elasticsearch

Evaluating search relevance part 1 - The BEIR benchmark

Evaluating search relevance part 2 - Phi-3 as relevance judge