Prompt-RAG：在小众领域开创无向量嵌入的检索增强生成，以韩医学为例
Prompt-RAG: Pioneering Vector Embedding-Free Retrieval-Augmented Generation in Niche Domains, Exemplified by Korean Medicine
- Gachon University（嘉泉大学）
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对小众领域中通用向量嵌入适用性不足的问题，本文提出无需嵌入向量的Prompt-RAG，并以韩医学QA验证其在相关性和信息性上优于ChatGPT及传统RAG。
AI中文摘要：
我们提出一种基于自然语言提示的检索增强生成（Prompt-RAG），这是一种提升生成式大型语言模型（LLM）在小众领域性能的新方法。传统RAG方法大多需要向量嵌入，但通用的基于LLM的嵌入表示对专业领域的适用性仍不确定。为探究并例证这一点，我们比较了来自韩医学（KM）和传统医学（CM）文档的向量嵌入，发现与CM嵌入相比，KM文档嵌入与token重叠的相关性更高，而与人工评估的文档相关性更低。与传统RAG模型不同，Prompt-RAG无需嵌入向量即可运行。其性能通过一个问答（QA）聊天机器人应用进行评估，其中响应从相关性、可读性和信息性三方面接受评价。结果表明，Prompt-RAG在相关性和信息性方面优于包括ChatGPT和传统基于向量嵌入的RAG在内的现有模型。尽管存在内容结构化和响应延迟等挑战，但LLM的进步预计将推动Prompt-RAG的应用，使其成为其他需要RAG方法的领域的有前景工具。
英文摘要：
We propose a natural language prompt-based retrieval augmented generation (Prompt-RAG), a novel approach to enhance the performance of generative large language models (LLMs) in niche domains. Conventional RAG methods mostly require vector embeddings, yet the suitability of generic LLM-based embedding representations for specialized domains remains uncertain. To explore and exemplify this point, we compared vector embeddings from Korean Medicine (KM) and Conventional Medicine (CM) documents, finding that KM document embeddings correlated more with token overlaps and less with human-assessed document relatedness, in contrast to CM embeddings. Prompt-RAG, distinct from conventional RAG models, operates without the need for embedding vectors. Its performance was assessed through a Question-Answering (QA) chatbot application, where responses were evaluated for relevance, readability, and informativeness. The results showed that Prompt-RAG outperformed existing models, including ChatGPT and conventional vector embedding-based RAGs, in terms of relevance and informativeness. Despite challenges like content structuring and response latency, the advancements in LLMs are expected to encourage the use of Prompt-RAG, making it a promising tool for other domains in need of RAG methods.
