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真正的定制或仅仅是营销:定制版本的生成AI有用吗?

Eduardo C Garrido-Merchán¹, Jose Luis Arroyo-Barrigüete^1,2, Francisco Borrás-Pala¹

¹Universidad Pontificia Comillas, Madrid, Community of Madrid, Spain.

|October 4, 2024

概括

定制的大型语言模型 (LLMs) 为统计学学生提供个性化的学习支持. 虽然像商业统计虚拟教授 (BSVP) 等量身定制的GPT显示了改进的沟通风格,但它们在整体质量方面并没有明显超过像ChatGPT-4 Turbo这样的一般模型.

科学领域:

教育中的人工智能
自然语言处理应用程序
教育技术的教育技术

背景情况:

大型语言模型 (LLM) 正在改变高等教育,定制化使得量身定制的学生支持成为可能.
OpenAI最近的功能允许基于自然语言接口的GPT模型针对特定任务进行定制.
这有助于为统计学等科目创建专门的AI导师.

研究的目的:

评估在高等教育中定制GPT的潜力.
为了评估和比较一个定制的商业统计虚拟教授 (BSVP) 与ChatGPT-4 Turbo的性能.
确定定制的LLM是否比统计教育的通用模型具有显著的优势.

主要方法:

为大学生开发了一个商业统计虚拟教授 (BSVP).
收集了统计学课程中的真实学生问题,并向BSVP和ChatGPT-4 Turbo提出了这些问题.
基于质量,深度和个性化的盲目评估反应,然后进行统计比较.

主要成果:

定制的BSVP表现出一种更容易理解和友好的沟通风格,包括幽默.
对于特定的请求,BSVP提供了卓越的响应,例如在R.的编程练习.
在BSVP和ChatGPT-4 Turbo之间在整体性能,质量,深度或路线对齐方面没有发现统计学上显著的差异.

结论:

定制的人工智能助理,经过特定提示的培训,为学生提供虚拟辅助的好处.
然而,这些专用型号并不代表比ChatGPT-4 Turbo等一般型号的实质性改进.
需要进一步的研究才能充分理解定制的LLM在教育环境中的影响.

关键词:

人工智能的人工智能聊天GPT 聊天在GPT 聊天定制化定制化定制化高等教育高等教育统计统计统计统计统计虚拟指导员虚拟指导员

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