基于大语言模型的电力领域知识问答方法

doi:10.19595/j.cnki.1000-6753.tces.251675

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Abstract Efficient and accurate acquisition of required knowledge from massive power field information sources poses a significant challenge to knowledge management in the power industry. Large language models (LLMs) have achieved breakthroughs in natural language understanding and generation, offering new avenues for knowledge question-answering. However, their application in the power field remains limited, as they struggle to comprehend specialized concepts and are prone to generating hallucinations. To address these issues, this paper proposes a knowledge question-answering method in the power field based on LLMs. This method improves the answer accuracy by integrating domain-specific knowledge bases with parameter-efficient fine-tuning optimization.
Firstly, this paper constructs a power field knowledge question-answering framework based on retrieval-augmented generation (P-RAG). This framework employs an adaptive text information extraction method based on LLMs to efficiently parse power document layouts and extract text. It then recursively segments the text based on hierarchical document titles to ensure semantic integrity, building a high-quality power field knowledge base. On this basis, prompt engineering guides the LLM to fully comprehend and utilize this power knowledge, thereby improving question-answering accuracy. Secondly, this paper proposes a model optimization method based on sample augmentation and LoRA fine-tuning. This method utilizes LLMs to rewrite and expand original question-answering pairs, diversifying textual expressions without altering the semantics, thereby effectively resolving the scarcity of fine-tuning samples. Meanwhile, the question and retrieved relevant knowledge are combined as the training input vector to construct a sample structure consistent with the actual input of the P-RAG framework. Through LoRA fine-tuning, the understanding of power field knowledge by LLMs and their ability to utilize external retrieved information are enhanced.
Comparative tests on a series of LLMs, including BaiChuan2-7B, BlueLM-7B, Qwen-7B, ChatGLM3-6B, and DeepSeek-R1-7B, show that after integration with the P-RAG framework, the performance of all participating models in power field knowledge question-answering tasks improved by 0.152 to 0.238. This verifies the universality and effectiveness of the P-RAG framework. Furthermore, based on Qwen-1.8B, Qwen-7B, and ChatGLM3-6B models, different fine-tuning optimization methods are compared. The results show that after standard LoRA fine-tuning optimization method, model performance decreased by approximately 0.035 to 0.104. In contrast, applying the proposed optimization method can improve the model performance by 0.024 to 0.124, validating the effectiveness of the proposed sample-augmentation fine-tuning optimization method.
The following conclusions can be drawn from the experimental analysis: (1) The proposed P-RAG framework provides external professional power knowledge for general LLMs, alleviating the hallucination problem of LLMs in the power field. (2) Compared with standard LoRA fine-tuning, the proposed model optimization method is more suitable for the P-RAG framework. Since the training sample input is consistent with the actual input of the P-RAG framework, it enhances the ability of LLMs to utilize external information and improves question-answering accuracy.

Key words： Large language models power knowledge question-answering retrieval-augmented generation sample augmentation parameter-efficient fine-tuning

Received: 28 September 2025

PACS:	TM73
	TP39

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	Yang Yan
	Liu Hao
	Song Jie
	Wu Yi

Cite this article:

Yang Yan,Liu Hao,Song Jie等. Knowledge Question-Answering Method in Power Field Based on Large Language Models[J]. Transactions of China Electrotechnical Society, 2026, 41(19): 6679-6692.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.251675 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I19/6679

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