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.

我们提出了一种迭代检索增强自我反馈 (RA-ISF) 框架，该框架将任务迭代分解并在三个子模块中处理，以增强模型的解决问题的能力。实验证明，我们的方法优于现有的基准，在像 GPT3.5、Llama2 这样的模型上表现良好，显著增强事实推理能力并减少虚幻现象。