当今通用人工智能(AGI)发展火热,各大语言模型(LLMs)层出不穷。大语言模型的广泛应用大大提高了人们的工作水平和效率,但大语言模型也并非完美的,同样伴随着诸多缺点。如:敏感数据安全性、幻觉性、时效性等。同时对于通用大语言模型来讲...当今通用人工智能(AGI)发展火热,各大语言模型(LLMs)层出不穷。大语言模型的广泛应用大大提高了人们的工作水平和效率,但大语言模型也并非完美的,同样伴随着诸多缺点。如:敏感数据安全性、幻觉性、时效性等。同时对于通用大语言模型来讲,对于一些专业领域问题的回答并不是很准确,这就需要检索增强生成(RAG)技术的支持。尤其是在智慧医疗领域方面,由于相关数据的缺乏,不能发挥出大语言模型优秀的对话和解决问题的能力。本算法通过使用Jieba分词,Word2Vec模型对文本数据进行词嵌入,计算句子间的向量相似度并做重排序,帮助大语言模型快速筛选出最可靠可信的模型外部的医疗知识数据,再根据编写相关的提示词(Prompt),可以使大语言模型针对医生或患者的问题提供令人满意的答案。Nowadays, general artificial intelligence is developing rapidly, and major language models are emerging one after another. The widespread application of large language models has greatly improved people’s work level and efficiency, but large language models are not perfect and are also accompanied by many shortcomings. Such as: data security, illusion, timeliness, etc. At the same time, for general large language models, the answers to questions in some professional fields are not very accurate, which requires the support of RAG technology. Especially in the field of smart medical care, due to the lack of relevant data, the excellent conversation and problem-solving capabilities of the large language model cannot be brought into play. This algorithm uses Jieba word segmentation and the Word2Vec model to embed text data, calculate the vector similarity between sentences and reorder them, helping the large language model to quickly screen out the most reliable and trustworthy medical knowledge data outside the model, and then write relevant prompts to enable the large language model to provide satisfactory answers to doctors or patients’ questions.展开更多
文摘当今通用人工智能(AGI)发展火热,各大语言模型(LLMs)层出不穷。大语言模型的广泛应用大大提高了人们的工作水平和效率,但大语言模型也并非完美的,同样伴随着诸多缺点。如:敏感数据安全性、幻觉性、时效性等。同时对于通用大语言模型来讲,对于一些专业领域问题的回答并不是很准确,这就需要检索增强生成(RAG)技术的支持。尤其是在智慧医疗领域方面,由于相关数据的缺乏,不能发挥出大语言模型优秀的对话和解决问题的能力。本算法通过使用Jieba分词,Word2Vec模型对文本数据进行词嵌入,计算句子间的向量相似度并做重排序,帮助大语言模型快速筛选出最可靠可信的模型外部的医疗知识数据,再根据编写相关的提示词(Prompt),可以使大语言模型针对医生或患者的问题提供令人满意的答案。Nowadays, general artificial intelligence is developing rapidly, and major language models are emerging one after another. The widespread application of large language models has greatly improved people’s work level and efficiency, but large language models are not perfect and are also accompanied by many shortcomings. Such as: data security, illusion, timeliness, etc. At the same time, for general large language models, the answers to questions in some professional fields are not very accurate, which requires the support of RAG technology. Especially in the field of smart medical care, due to the lack of relevant data, the excellent conversation and problem-solving capabilities of the large language model cannot be brought into play. This algorithm uses Jieba word segmentation and the Word2Vec model to embed text data, calculate the vector similarity between sentences and reorder them, helping the large language model to quickly screen out the most reliable and trustworthy medical knowledge data outside the model, and then write relevant prompts to enable the large language model to provide satisfactory answers to doctors or patients’ questions.
