例如
ChatGPT—1:参数量达1.17亿,预训练数据量约5GB。
1.17 亿 gpt1 15 亿 gpt2 1750 亿 gtp3.5 万亿 M6 GTP4 1.5万亿
看图,了解一下几代模型的差异如下:
要更直观的了解模型间的差异,可以直观去体验一下。目前GPT2是开源的,然后市面上主要用的是GPT3.5和GPT4,可以在这里免费使用: go.aitool123.cc
研究背景 Purpose
Chatbots, which are based on large language models, are increasingly being used in public health. However, the effectiveness of chatbot responses has been debated, and their performance in myopia prevention and control has not been fully explored. This study aimed to evaluate the effectiveness of three well-known chatbots—ChatGPT, Claude, and Bard—in responding to public health questions about myopia.
基于大型语言模型的聊天机器人越来越多地用于公共卫生领域。然而,聊天机器人响应的有效性一直存在争议,它们在近视预防和控制方面的表现尚未得到充分探索。本研究旨在评估三个知名聊天机器人——ChatGPT、Claude 和 Bard——在回答有关近视的公共卫生问题方面的有效性。
研究方法 Methods
Nineteen public health questions about myopia (including three topics of policy, basics and measures) were responded individually by three chatbots. After shuffling the order, each chatbot response was independently rated by 4 raters for comprehensiveness, accuracy and relevance.
三个聊天机器人分别回答了 19 个关于近视的公共卫生问题(包括政策、基础知识和措施三个主题)。在对顺序进行排序后,每个聊天机器人的响应都由 4 位评分者独立评分,以评估其全面性、准确性和相关性。
研究结果 Results
The study’s questions have undergone reliable testing. There was a significant difference among the word count responses of all 3 chatbots. From most to least, the order was ChatGPT, Bard, and Claude. All 3 chatbots had a composite score above 4 out of 5. ChatGPT scored the highest in all aspects of the assessment. However, all chatbots exhibit shortcomings, such as giving fabricated responses.
该研究的问题已经过可靠的测试。所有 3 个聊天机器人的字数响应之间存在显着差异。从多到少,顺序是 ChatGPT、Bard 和 Claude。所有 3 个聊天机器人的综合得分都在 4 分(满分 5 分)以上。ChatGPT 在评估的所有方面都得分最高。但是,所有聊天机器人都表现出缺点,例如给出捏造的回复。
研究结论 Conclusion
Chatbots have shown great potential in public health, with ChatGPT being the best. The future use of chatbots as a public health tool will require rapid development of standards for their use and monitoring, as well as continued research, evaluation and improvement of chatbots.
聊天机器人在公共卫生方面显示出巨大的潜力,其中 ChatGPT 是最好的。未来将聊天机器人用作公共卫生工具将需要快速制定其使用和监控标准,以及对聊天机器人的持续研究、评估和改进。
本文转载自Journal of multidisciplinary healthcare
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