report[Jarvis and Allard, 2023]
optimizing
techniques

including RAG and

have
large
its

(LLMs),

various

for

Additionally,

like RAGAS[Es et al., 2023]

evalu-
evaluation metrics.
ation
and
frameworks
ARES[Saad-Falcon et al., 2023] also involve RAG eval-
uation metrics. Summarizing these works, three core metrics
are primarily focused on: Faithfulness of the answer, Answer
Relevance, and Context Relevance.

latest

the

1. Faithfulness

This metric emphasizes that the answers generated by
the model must remain true to the given context, ensur-
ing that the answers are consistent with the context infor-
mation and do not deviate or contradict it. This aspect of
evaluation is vital for addressing illusions in large mod-
els.

2. Answer Relevance

This metric stresses that the generated answers need to
be directly related to the posed question.

3. Context Relevance
Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey