RA-ISF (Retrieval Augmented Iterative Self-Feedback) is a novel framework designed to enhance the performance of large language models (LLMs) in open-domain question answering tasks. The framework addresses the limitations of traditional retrieval-augmented generation (RAG) methods by introducing an iterative self-feedback mechanism that decomposes and processes questions through three submodules: Self-Knowledge Module, Passage Relevance Module, and Question Decomposition Module. These submodules work together to assess the model's ability to solve problems using its own knowledge, evaluate the relevance of retrieved passages, and break down complex questions into simpler sub-questions. The framework aims to improve the model's problem-solving capabilities, reduce hallucinations, and enhance factual reasoning. The key contributions of RA-ISF include: 1. **Iterative Self-Feedback**: A novel approach that iteratively processes questions to enhance the model's problem-solving capabilities. 2. **Task Decomposition**: Breaks down complex questions into simpler sub-questions to improve the model's ability to handle intricate tasks. 3. **Enhanced Knowledge Retrieval**: Significantly improves the model's performance in handling complex questions across various datasets. Experiments on multiple LLMs (GPT3.5 and Llama2) demonstrate that RA-ISF outperforms existing benchmarks, achieving superior performance in factual reasoning and reducing hallucinations. The framework's effectiveness is validated through ablation studies and human evaluations, showing that each component of RA-ISF contributes positively to the overall performance.RA-ISF (Retrieval Augmented Iterative Self-Feedback) is a novel framework designed to enhance the performance of large language models (LLMs) in open-domain question answering tasks. The framework addresses the limitations of traditional retrieval-augmented generation (RAG) methods by introducing an iterative self-feedback mechanism that decomposes and processes questions through three submodules: Self-Knowledge Module, Passage Relevance Module, and Question Decomposition Module. These submodules work together to assess the model's ability to solve problems using its own knowledge, evaluate the relevance of retrieved passages, and break down complex questions into simpler sub-questions. The framework aims to improve the model's problem-solving capabilities, reduce hallucinations, and enhance factual reasoning. The key contributions of RA-ISF include: 1. **Iterative Self-Feedback**: A novel approach that iteratively processes questions to enhance the model's problem-solving capabilities. 2. **Task Decomposition**: Breaks down complex questions into simpler sub-questions to improve the model's ability to handle intricate tasks. 3. **Enhanced Knowledge Retrieval**: Significantly improves the model's performance in handling complex questions across various datasets. Experiments on multiple LLMs (GPT3.5 and Llama2) demonstrate that RA-ISF outperforms existing benchmarks, achieving superior performance in factual reasoning and reducing hallucinations. The framework's effectiveness is validated through ablation studies and human evaluations, showing that each component of RA-ISF contributes positively to the overall performance.