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The aim is to enhance knowledge question answering in the electric power domain by leveraging retrieval-augmented generation technology.

February 17, 2026Open Access

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

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The aim is to enhance knowledge question answering in the electric power domain by leveraging retrieval-augmented generation technology.
Utilized an LLM to generate hypothetical documents.
Combined original questions with generated documents for similarity retrieval.
Processed combined content to retrieve relevant documents in a two-step generation approach.
Enhanced attention to relevant documents by incorporating irrelevant documents into context.
Conducted experiments on electricity and CMRC datasets.
Achieved a 4.63% improvement in the ROUGE-L metric over the naive RAG method.
Obtained an 11.32% improvement in the BLEU-4 metric on the electricity dataset.
Validated effectiveness on the public CMRC dataset.

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Demonstrates enhanced knowledge question answering in the electric power domain using a novel retrieval method, suggesting improved reliability.

Chen et al. (2026) studied this question.

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Demonstrates enhanced knowledge question answering in the electric power domain using a novel retrieval method, suggesting improved reliability.