用外部知识增强大模型，减少中小企业误信息风险。
Enhancing LLMs with Context-Specific Knowledge for Mitigating Misinformation in SMEs: A RAG-based Modeling and Analysis
- 引入向量与图结构RAG，结合企业特定知识库
- 在多个大模型上降低幻觉率，提升回答可信度
- 适合需可靠决策支持的中小企业场景
大型语言模型（LLMs）正被越来越多的小中型企业（SMEs）用于提升问答能力与辅助决策。然而，生成内容中的幻觉会引发误信息，削弱用户对其可靠性与可信度的信任。检索增强生成（RAG）通过引入外部知识源，成为缓解该问题的有前景方法。本文提出向量RAG与图RAG建模方法，在多种先进大模型（包括LLaMA、Mistral和Qwen）上评估其在小中型企业环境中的表现，涵盖有效回应生成、幻觉风险、上下文相关性及人工可解释性等指标。结果表明，经RAG增强的模型能显著降低幻觉与误信息风险，提升响应质量，支持更可靠、可信且情境感知的决策。
原文摘要 · Abstract (English)
Large Language Models (LLMs), a part of artificial intelligence (AI), are increasingly being adopted by Small and Medium Enterprises (SMEs) to enhance question-answering capabilities and support business decision-making processes. However, hallucinations in LLM-generated outputs can serve as a source of misinformation, reducing user confidence in their reliability and trustworthiness within SMEs. Retrieval-Augmented Generation (RAG) has emerged as a promising approach to address this challenge by incorporating external knowledge sources into the modeling process. In this paper, we present VectorRAG and GraphRAG modeling approaches to mitigate hallucinations and misinformation risks and evaluate their effectiveness in SME environments. Our experimental evaluation is conducted on multiple state-of-the-art LLMs, including LLaMA, Mistral, and Qwen, to assess performance in terms of useful response generation, risk of hallucination, contextual relevance, as well as human-interpretation. The results demonstrate that RAG-enhanced LLMs can significantly improve response quality by reducing hallucinations and misinformation, thereby supporting more reliable, trustworthy, and context-aware decision-making in SME environments.
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