A Multi-View Knowledge-Enhanced Approach for Mitigating Factual Hallucinations in Large Language Models

doi:10.3969/j.issn.1671-1122.2026.05.009

Abstract

Abstract:

Factual hallucination refers to the phenomenon where large language models generate text that is contextually inconsistent or contradictory to established facts during the text generation process. This issue not only undermines the reliability of the generated text but may also lead to the dissemination of misinformation, thereby negatively impacting user decision-making. To address this challenge, this paper proposed a novel hallucination mitigation method based on a Multi-View Knowledge Graph (MVKG) and Direct Preference Optimization (DPO). The method introduced MVKG and employed the large language model itself to update the knowledge graph. Simultaneously, it adopted a multi-view keyword joint retrieval mechanism, combined with multi-view direct preference optimization, to enhance the model’s ability to extract multi-view entities from queries, thereby improving the overall relevance of retrieval. Experimental results demonstrate that, compared to existing Retrieval-Augmented Generation (RAG) methods, the proposed method significantly improves question-answering accuracy on both the Qwen series of models and closed-source models, effectively mitigating the factual hallucination problem in large language models.

Key words: hallucination of LLM, retrieval-augmented generation, knowledge graph, DPO

CLC Number:

TP309

HU Qingcheng, ZHANG Wan, YUAN Yali, ZHANG Jing. A Multi-View Knowledge-Enhanced Approach for Mitigating Factual Hallucinations in Large Language Models[J]. Netinfo Security, 2026, 26(5): 772-787.

Figures/Tables 9

References 39

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负样本类型	查询	优选响应
基线模型不准确输出	What are the common methods for supervised learning	Macro:[learning paradigm, model type] Micro:[support vector machine,decision tree]
逻辑问题：层次错配	Explain Newton’s laws of motion	Macro:[physical principles, force interaction] Micro:[inertia, F=ma]
逻辑问题：属性嫁接	What are the functions of common cold medicines	Macro:[symptom relief, pathogen inhibition] Micro:[reduce fever,antiviral]
逻辑问题：概念泛化	Compare different battery technologies	Macro:[energy density,cycle life] Micro:[lithium-ion,lead-acid]
随机丢弃关键信息	How does a content-based recommendation system work	Macro:[feature extraction, similarity matching] Micro:[TF-IDF,cosine similarity]
负样本类型	劣选响应	构造逻辑说明
基线模型不准确输出	Macro:[algorithm, method] Micro:[clustering,backpropagation]	基础模型输出错误：微观词“Clustering”是无监督方法,“Backpropagation”是优化技术,均与“监督学习”查询不匹配
逻辑问题：层次错配	Macro:[Net force,vector superposition method] Micro:[inertia, F=ma]	正确的微观关键词“矢量叠加”、“合力”被错误地关联到宏观主题之下
逻辑问题：属性嫁接	macro:[symptom relief, pathogen inhibition] Micro:[reduce fever,conductivity]	将物理属性“conductivity”错误嫁接至药品功能的微观描述中,造成语义混淆
逻辑问题：概念泛化	Macro:[energy density,cycle life] Micro:[battery,technology]	具体的技术实例被替换为过于空泛的上位词“Battery”和“Technology”,信息量严重不足
随机丢弃关键信息	Macro:[feature extraction, similarity matching] Micro:[cosine similarity]	随机制造信息残缺,此例中缺失了关键的文本相似度度量方法“TF-IDF”

信息	农业	计算机	法律	混合
总文档数/个	12	10	94	61
总token数/个	2017886	2306535	5081069	619009

模型	Naive RAG	GraphRAG	MemoRAG	MVRAG w/o MV-DPO	MVRAG with MV-DPO
Qwen3-8B	54.0%	68.7%	63.7%	72.9%	74.6%
Qwen3-14B	60.4%	76.7%	80.9%	81.4%	83.3%
ChatGPT-3.5	79.5%	83.5%	85.3%	86.1%	—
gemini-2.0	80.1%	88.6%	89.0%	89.2%	—

模型	Naive RAG				GraphRAG
模型	农业	计算机	法律	混合	农业	计算机	法律	混合
Qwen3-8B	55.3%	57.8%	51.0%	57.8%	68.9%	63.8%	72.3%	63.8%
Qwen3-14B	61.8%	64.6%	57.0%	64.6%	77.0%	71.3%	80.8%	71.3%
ChatGPT-3.5	75.3%	78.1%	80.9%	78.4%	81.7%	75.8%	85.5%	82.1%
gemini-2.0	77.1%	75.4%	79.6%	82.3%	82.3%	81.9%	91.5%	86.6%
模型	MemoRAG				MVRAG w/o MV-DPO
模型	农业	计算机	法律	混合	农业	计算机	法律	混合
Qwen3-8B	69.3%	51.2%	64.9%	62.7%	73.1%	68.0%	76.5%	68.0%
Qwen3-14B	71.1%	71.5%	84.2%	79.3%	81.7%	76.0%	85.5%	76.0%
ChatGPT-3.5	82.9%	80.3%	87.6%	83.0%	86.1%	80.6%	90.4%	80.3%
gemini-2.0	89.1%	85.2%	93.7%	82.4%	89.7%	77.1%	94.1%	83.6%
模型	MVRAG with MV-DPO				—	—	—	—
模型	农业	计算机	法律	混合	—	—	—	—
Qwen3-8B	74.8%	69.7%	78.2%	69.7%	—	—	—	—
Qwen3-14B	83.6%	77.9%	87.4%	77.9%	—	—	—	—
ChatGPT-3.5	—	—	—	—	—	—	—	—
gemini-2.0	—	—	—	—	—	—	—	—

模型	Naive RAG			GraphRAG
模型	LLM Score	MC1 Score	MC2 Score	LLM Score	MC1 Score	MC2 Score
Qwen3-4B	56.2%	18.1%	23.9%	58.2%	28.7%	26.1%
Qwen3-8B	52.3%	21.4%	33.1%	55.0%	25.2%	36.4%
Qwen3-14B	67.7%	28.5%	39.2%	68.5%	32.1%	42.8%
ChatGPT-3.5	64.3%	33.9%	46.1%	66.4%	38.3%	48.1%
gemini-2.0	69.2%	45.8%	37.9%	69.8%	48.9%	43.3%
模型	MemoRAG			MVRAG
模型	LLM Score	MC1 Score	MC2 Score	LLM Score	MC1 Score	MC2 Score
Qwen3-4B	51.9%	27.2%	28.3%	63.7%	22.9%	28.8%
Qwen3-8B	56.8%	29.1%	37.2%	58.5%	28.4%	39.1%
Qwen3-14B	68.6%	34.0%	42.1%	69.5%	35.8%	45.0%
ChatGPT-3.5	67.4%	41.9%	40.3%	68.1%	42.2%	49.3%
gemini-2.0	68.2%	50.2%	44.5%	73.1%	49.6%	45.1%