• EMDR2. By using the Expectation-Maximization algo-
rithm, this method trains the model with retrieved docu-
ments as latent variables.

• Perplexity Distillation directly trains the model using the

perplexity of generated tokens as an indicator.

• LOOP. This method presents a novel loss function based
on the impact of document deletion on LLM prediction,
offering an efficient training strategy to better adapt the
model to specific tasks.

These approaches aim to improve the synergy between the
retriever and the LLM, leading to enhanced retrieval perfor-
mance and more accurate responses to user inquiries.
Adapters
Fine-tuning models may present challenges, such as integrat-
ing functionality through an API or addressing constraints
arising from limited local computational resources. Con-
sequently, some approaches opt to incorporate an external
adapter to aid in alignment.
RAG forLargeLanguageModels-ASurvey