[论文解读] A Glimpse in ChatGPT Capabilities and its impact for AI research

Frank Joublin, Antonello Ceravola+4 more|arXiv (Cornell University)|May 10, 2023

Artificial Intelligence in Healthcare and Education被引用 13

一句话总结

本文综述 GPT-3.5/3.4 和 GPT-4 的能力，讨论 AI 研究的成本与资源影响，并认为能力的广泛范围表明向通用智能迈进，带来下游社会影响。

ABSTRACT

Large language models (LLMs) have recently become a popular topic in the field of Artificial Intelligence (AI) research, with companies such as Google, Amazon, Facebook, Amazon, Tesla, and Apple (GAFA) investing heavily in their development. These models are trained on massive amounts of data and can be used for a wide range of tasks, including language translation, text generation, and question answering. However, the computational resources required to train and run these models are substantial, and the cost of hardware and electricity can be prohibitive for research labs that do not have the funding and resources of the GAFA. In this paper, we will examine the impact of LLMs on AI research. The pace at which such models are generated as well as the range of domains covered is an indication of the trend which not only the public but also the scientific community is currently experiencing. We give some examples on how to use such models in research by focusing on GPT3.5/ChatGPT3.4 and ChatGPT4 at the current state and show that such a range of capabilities in a single system is a strong sign of approaching general intelligence. Innovations integrating such models will also expand along the maturation of such AI systems and exhibit unforeseeable applications that will have important impacts on several aspects of our societies.

研究动机与目标

• 在日益增长的 GAFA 规模投资背景下，推动在 AI 研究中对大语言模型 (LLMs) 的研究。
• 展示单一系统内类似 ChatGPT 的模型的广泛能力。
• 突出限制非 GAFA 研究实验室的资源与硬件成本。
• 讨论随着 LLMs 的成熟，潜在的创新与不可预见的应用。

提出的方法

• 提供在研究任务中使用 GPT-3.5/ChatGPT 3.4 和 GPT-4 的示例。
• 对能力范围及其对研究实践的影响进行定性分析。
• 论证能力的广度表明正在接近通用智能。
• 讨论不断演进的 AI 系统如何催生不可预见的应用及社会影响。

实验结果

研究问题

• RQ1 GPT-3.5/3.4 和 GPT-4 在研究环境中展示了哪些能力？
• RQ2 用于训练和运行这些模型于研究实验室的资源与硬件成本是多少？
• RQ3 这些模型的能力如何影响 AI 研究方向与创新轨迹？
• RQ4 观察到的能力是否表明向通用智能的进步，以及更广泛的社会影响是什么？

主要发现

• 这些模型展现出可用于研究任务的广泛能力。
• 像 ChatGPT 这样的单一系统展现出涵盖翻译、生成与推理的多样化功能。
• 能力的广度被解读为接近通用智能的有力信号。
• 将此类模型整合的创新有望随着成熟而扩展，并带来不可预见的应用。