RA - ISF : Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback (2024)

Abstract: 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.

## introduction large language models (llms) @xcite @xcite have demonstrated their excellent performance in knowledge reasoning and outstanding capabilities across various task domain @xcite . however, the parameterized knowledge stored within llms may be incomplete and hard to incorporate up-to-date knowledge @xcite . to address this issue, retrieval-augmented generation (rag) approaches can leverage external knowledge and documents, extract non-parameterized knowledge, and incorporate it into the model's prompts, thereby embedding new knowledge into the language model @xcite . this approach demonstrates outstanding performance in answering a variety of open-domain questions. however, current rag frameworks have two major challenges. first, retrieving irrelevant knowledge texts will impair the llms' ability to solve tasks @xcite . second, the incorporation of llm's existing knowledge and the retrieved knowledge may face difficulty @xcite . some methods have conducted research based on these issues, including considering the model's problem-solving abilities @xcite and whether the retrieved passages are relevant to the question @xcite @xcite . however, current solutions still have drawbacks in answering knowledge-intensive questions and different levels of sub-questions. therefore, how to fuse knowledge and utilize knowledge for question answering is very important in this process. to overcome the above limitations, we introduce retrieval augmented iterative self-feedback (ra-isf), a framework addresses problems by iteratively processing questions. specifically, unlike directly appending retrieved knowledge into prompts, our approach employs three sub-modules for iterative processing. these three sub-modules are the self-knowledge module, the passage relevance module, and the question decomposition module. we have also collected a series of data through llms to evaluate whether a specific module possesses the corresponding capabilities. by training a small language model or simply relying on incontext learning, these modules can demonstrate capabilities in self-knowledge, relevance judgment, and question decomposition. as shown in figure 1 , ra-isf first uses a selfknowledge module to determine whether the current question could be answered on its own knowledge. then, when employing a retrieval strategy, the passage relevance module will assess the rel-evance of each retrieved paragraph to the problem. relevant paragraphs will be integrated into the prompt and used for prediction. when all paragraphs are irrelevant to the question, the question decomposition module will break down the questions into sub-questions and repeat the aforementioned steps for these sub-questions. ultimately, the model will synthesize the answers to the subquestions to respond to the original question. compared to previous rag methods, our iterative self-feedback approach more effectively unleashes the potential of the model and better incorporates external knowledge with the model's inherent knowledge. simultaneously, ra-isf can address questions by decomposing them when the model lacks an initial answer or retrieves irrelevant texts, combining these solutions to answer the origin question, which is an effective problemsolving strategy. experiments on various llms (e.g., gpt3.5 @xcite and @xcite ) demonstrate that ra-isf exhibits superior performance in handling complex questions compared to existing methods. ## methodology existing retrieval augmented methods still have some shortcomings. for instance, the model may struggle to solve problems based solely on its own knowledge, and during retrieval, it might be influenced by irrelevant texts, leading to the generation of incorrect answers. therefore, we introduce an upgraded retrieval-augmented generation framework -retrieval augmented iterative self-feedback (ra-isf), which improves the quality and accuracy of llm responses through internal knowledge comprehension, external knowledge retrieval, and problem decomposition. ## conclusions in this paper, we introduce ra-isf, a framework designed to enhance retrieval augmentation effects and improve performance in open-domain question answering. this approach effectively mitigates the hallucination issues that are commonly seen in traditional retrieval augmentation and questionanswering tasks. experimental results demonstrate ra-isf's superior performance across various benchmarks, and ablation studies validate the effectiveness of sub-modules. future research directions include further alleviating hallucination issues and improving the efficiency of the framework.