Data Processing

Requires minimal data processing and han-
dling.

Stores static data, requiring retraining for
knowledge and data updates.

Can be applied to align the externally learned
knowledge from pretraining with large lan-
guage models, but may be less practical for
frequently changing data sources.

Relies on constructing high-quality datasets,
and limited datasets may not yield significant
performance improvements.

Model Customization

Focuses on information retrieval and inte-
grating external knowledge but may not fully
customize model behavior or writing style.

Allows adjustments of LLM behavior, writ-
ing style, or specific domain knowledge
based on specific tones or terms.

Interpretability

Answers can be traced back to specific data
sources, providing higher interpretability and
traceability.

Like a black box, not always clear why the
model reacts a certain way, with relatively
lower interpretability.

Computational Resources
Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey