A comparative study of chatbot response generation: traditional approaches versus large language models

Michael McTear, Sheen Varghese Marokkie, Y Bi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Chatbot responses can be generated using traditional rule-based conversation design or through the use of large language models (LLMs). In this paper we compare the quality of responses provided by LLM-based chatbots with those provided by traditional conversation design. The results suggest that in some cases the use of LLMs could improve the quality of chatbot responses. The paper concludes by suggesting that a combination of approaches is the best way forward and suggests some directions for future work.

Original languageEnglish
Title of host publicationKnowledge Science, Engineering and Management - 16th International Conference, KSEM 2023
EditorsZhi Jin, Yuncheng Jiang, Wenjun Ma, Robert Andrei Buchmann, Ana-Maria Ghiran, Yaxin Bi
Place of PublicationGermany
Number of pages10
VolumeLNCS, volume 14118
ISBN (Electronic)978-3-031-40285-2
Publication statusPublished online - 9 Aug 2023
Event16th International Conference, KSEM 2023, - China, Guangzhou
Duration: 16 Aug 202319 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14118 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference, KSEM 2023,
Abbreviated titleKSEM 2023
Internet address

Bibliographical note

Funding Information:
Michael McTear received support from the e-VITA project (https://www.e-vita.coach/) (accessed on 23 April 2023). Sheen Varghese Marokkie and Yaxin Bi received support from the School of Computing, Ulster University (https://www.ulster.ac.uk/faculties/computing-engineering-and-the-built-environme nt/computing).

Funding Information:
1The e-VITA project has received funding from the European Union H2020 Pro-gramme under grant agreement no. 101016453. The Japanese consortium received funding from the Japanese Ministry of Internal Affairs and Communication (MIC), Grant no. JPJ000595.

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • ChatGTP
  • NLP
  • ChatGPT
  • Conversational AI
  • Conversation design
  • Rasa
  • Large Language Models
  • Bard


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