RD-P: A Trustworthy Retrieval-Augmented Prompter with Knowledge Graphs for LLMs

Large Language Models (LLMs) face challenges due to hallucination issues. Current solutions use retrieval-augmented generation (RAG), integrating LLMs with external knowledge to enhance answer accuracy. However, the misuse of irrelevant external knowledge can be misleading. In this paper, we propose a novel method called Retrieve-and-Discriminate Prompter (RD-P), which leverages knowledge graphs (KGs) for trustworthy RAG by synchronizing knowledge retrieval and discrimination in a unified model. Specifically, we train a prompter based on a pre-trained language model with shared parameters. It has two key modules: the retriever and the discriminator. The retriever identifies relevant reasoning paths in the KG, while the discriminator evaluates their credibility through "logical coverage calculation" and in turn instructs the retrieval process. Prompts are then constructed to guide LLMs in reasoning and answering questions using both retrieved and implicit knowledge. Experiments on knowledge-intensive question answering (QA) tasks demonstrate that our method significantly improves answer coverage rate while reducing the retrieval scale, achieving superior performance in complex KGQA tasks compared with state-of-the-art RAG methods at a low cost.