ChatGPT versus Bard: A comparative study

被引:2
|
作者
Ahmed, Imtiaz [1 ]
Kajol, Mashrafi [2 ]
Hasan, Uzma [3 ]
Datta, Partha Protim [4 ]
Roy, Ayon [5 ]
Reza, Md. Rokonuzzaman [6 ]
机构
[1] New Mexico Inst Min & Technol, Dept Comp Sci & Engn, Socorro, NM USA
[2] Univ New Hampshire, Dept Comp Sci, Durham, NH USA
[3] Univ Maryland Baltimore Cty, Dept Comp Sci, Baltimore, MD USA
[4] Univ North Florida, Dept Comp Sci, Jacksonville, FL USA
[5] Mil Inst Sci & Technol, Dept Comp Sci, Dhaka, Bangladesh
[6] Int Univ Scholars, Dept Comp Sci, Dhaka, Bangladesh
关键词
artificial hallucination; artificial intelligence; attention mechanism; Bard; ChatGPT; fairness; generative AI; GPT-3.5; GPT-4; human interaction; LaMDA; LLM; NLP; PaLM; 2; token; transformer architecture; EMPIRICAL-ANALYSIS;
D O I
10.1002/eng2.12890
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The rapid progress in conversational AI has given rise to advanced language models capable of generating human-like texts. Among these models, ChatGPT and Bard, developed by OpenAI and Google AI respectively, have gained significant attention. With their wide range of functionalities, such as human-like response generation, proficiency in professional exams, complex problem solving, and more, these models have captured interest. This study presents a comprehensive survey exploring and comparing the capabilities and features of ChatGPT and Bard. We delve into their architectures, training methodologies, performance evaluations, and limitations across various domains. Ethical considerations such as biases and potential misconduct are also examined. Our findings highlight ChatGPT's exceptional performance, positioning it as a leading model. This survey is a vital resource for scholars, innovators, and interested parties operating within the domain of conversational artificial intelligence, offering valuable insights for the advancement of cutting-edge language models. This article presents a comprehensive survey that explores and compares the capabilities and features of ChatGPT and Bard. We delve into their architectures, training methodologies, performance evaluations, and limitations across various domains. Ethical considerations such as biases and potential misconduct are also examined. image
引用
收藏
页数:18
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