TY - GEN
T1 - Comparing Generative Chatbots Based on Process Requirements
T2 - 2023 IEEE International Conference on Big Data, BigData 2023
AU - Lins, Luis Fernando
AU - Nascimento, Nathalia
AU - Alencar, Paulo
AU - Oliveira, Toacy
AU - Cowan, Donald
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Business processes are commonly represented by modelling languages, such as Event-driven Process Chain (EPC), Yet Another Workflow Language (YAWL), and the most popular standard notation for modelling business processes, the Business Process Model and Notation (BPMN). Most recently, chatbots, programs that allow users to interact with a machine using natural language, have been increasingly used for business process execution support. A recent category of chatbots worth mentioning is generative-based chatbots, powered by Large Language Models (LLMs) such as OpenAI's Generative Pre-Trained Transformer (GPT) model and Google's Pathways Language Model (PaLM), which are trained on billions of parameters and support conversational intelligence. However, it is not clear whether generative-based chatbots are able to understand and meet the requirements of constructs such as those provided by BPMN for process execution support. This paper presents a case study to compare the performance of prominent generative models, GPT and PaLM, in the context of process execution support. The research sheds light into the challenging problem of using conversational approaches supported by generative chatbots as a means to understand process-aware modelling notations and support users to execute their tasks.
AB - Business processes are commonly represented by modelling languages, such as Event-driven Process Chain (EPC), Yet Another Workflow Language (YAWL), and the most popular standard notation for modelling business processes, the Business Process Model and Notation (BPMN). Most recently, chatbots, programs that allow users to interact with a machine using natural language, have been increasingly used for business process execution support. A recent category of chatbots worth mentioning is generative-based chatbots, powered by Large Language Models (LLMs) such as OpenAI's Generative Pre-Trained Transformer (GPT) model and Google's Pathways Language Model (PaLM), which are trained on billions of parameters and support conversational intelligence. However, it is not clear whether generative-based chatbots are able to understand and meet the requirements of constructs such as those provided by BPMN for process execution support. This paper presents a case study to compare the performance of prominent generative models, GPT and PaLM, in the context of process execution support. The research sheds light into the challenging problem of using conversational approaches supported by generative chatbots as a means to understand process-aware modelling notations and support users to execute their tasks.
UR - https://www.scopus.com/pages/publications/85184978463
UR - https://www.scopus.com/pages/publications/85184978463#tab=citedBy
U2 - 10.1109/BigData59044.2023.10386251
DO - 10.1109/BigData59044.2023.10386251
M3 - Conference contribution
AN - SCOPUS:85184978463
T3 - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
SP - 4664
EP - 4673
BT - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
A2 - He, Jingrui
A2 - Palpanas, Themis
A2 - Hu, Xiaohua
A2 - Cuzzocrea, Alfredo
A2 - Dou, Dejing
A2 - Slezak, Dominik
A2 - Wang, Wei
A2 - Gruca, Aleksandra
A2 - Lin, Jerry Chun-Wei
A2 - Agrawal, Rakesh
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 December 2023 through 18 December 2023
ER -