  Large language models (LLMs) demonstrate exceptional performance in numerous
tasks but still heavily rely on knowledge stored in their parameters. Moreover,
updating this knowledge incurs high training costs. Retrieval-augmented
generation (RAG) methods address this issue by integrating external knowledge.
The model can answer questions it couldn't previously by retrieving knowledge
relevant to the query. This approach improves performance in certain scenarios
for specific tasks. However, if irrelevant texts are retrieved, it may impair
model performance. In this paper, we propose Retrieval Augmented Iterative
Self-Feedback (RA-ISF), a framework that iteratively decomposes tasks and
processes them in three submodules to enhance the model's problem-solving
capabilities. Experiments show that our method outperforms existing benchmarks,
performing well on models like GPT3.5, Llama2, significantly enhancing factual
reasoning capabilities and reducing hallucinations.
