Virtual-Document-Augmented Retrieval-Augmented Generation for Power-Domain Knowledge-Base Question Answering with Noise-Enhanced Robustness

The Retrieval-Augmented Generation (RAG) technology enables large language models (LLM) to access external knowledge bases by introducing external documents, enhancing their capability for knowledge question answering in professional domains and generating more reliable responses. It effectively addresses issues such as LLM hallucinations and knowledge obsolescence. In the electric power domain, RAG technology can be leveraged to fully utilize accumulated corporate data and resources. However, in the retrieval phase of RAG, there are significant differences in the semantic space representation between short sentences and long text documents. Additionally, when generating answers based on retrieved relevant documents, the generator prioritizes highly relevant document fragments, a strategy that may overlook sub-relevant documents containing useful information. This paper uses an LLM to generate hypothetical documents. These documents are combined with the original question to perform similarity retrieval in the corpus, followed by the first round of answer generation. Subsequently, the original question is combined with the answer generated in the first round, and this combined content is used to retrieve relevant documents. Finally, irrelevant documents are added to the context of the retrieved relevant documents to enhance the LLM’s attention to the relevant documents. Based on the above strategies, experiments are conducted on the electricity dataset. The results show that, compared with the naive RAG method, the proposed model achieves a relative improvement of 4.63% in the ROUGE-L metric and 11.32% in the BLEU-4 metric on the electricity dataset. Meanwhile, experiments are also carried out on the public CMRC dataset, and the effectiveness of the proposed method is verified.