用向量嵌入整合外部信息，减少大模型幻觉。
LLM Enhancer: Merged Approach using Vector Embedding for Reducing Large Language Model Hallucinations with External Knowledge
- 通过向量嵌入筛选谷歌、维基等多源信息
- 在保持对话自然性前提下提升回答准确性
- 适合需高可靠性的问答与对话系统
大型语言模型（如ChatGPT）在任务导向对话和问答中展现出生成类人自然响应的能力，但在真实关键场景中常因产生错误信息且难以利用外部知识而受限。本文提出LLM ENHANCER系统，集成谷歌、维基百科和DuckDuckGo等多个在线来源以增强数据准确性。系统采用开源大模型，数据获取过程并行化，通过定制代理工具管理信息流。利用向量嵌入识别最相关的信息，并将其提供给大模型用于用户交互。该系统在保持响应自然性的基础上有效缓解了基于聊天的大模型幻觉问题。
原文摘要 · Abstract (English)
Large Language Models (LLMs), such as ChatGPT, have demonstrated the capability to generate human like, natural responses across a range of tasks, including task oriented dialogue and question answering. However, their application in real world, critical scenarios is often hindered by a tendency to produce inaccurate information and a limited ability to leverage external knowledge sources. This paper introduces the LLM ENHANCER system, designed to integrate multiple online sources such as Google, Wikipedia, and DuckDuckGo to enhance data accuracy. The LLMs employed within this system are open source. The data acquisition process for the LLM ENHANCER system operates in parallel, utilizing custom agent tools to manage the flow of information. Vector embeddings are used to identify the most pertinent information, which is subsequently supplied to the LLM for user interaction. The LLM ENHANCER system mitigates hallucinations in chat based LLMs while preserving response naturalness and accuracy.
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