TY - GEN
T1 - A Retrieval-based Chatbot for Customer Service Using Large Language Model
AU - Huang, Pei Chi
AU - Pan, Mei Lien
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The surge in service requests at the Information Technology Service Center at the beginning of each semester poses a substantial strain on human resources, necessitating innovative solutions for efficient service management. With advancements in Natural Language Processing (NLP), AI chatbots have been introduced to address this challenge. This paper implements an AI chatbot utilizing OpenAI GPT-3.5 on the Information Technology Service Center website, discovering its capabilities of handling miscellaneous tasks. Our key contribution is to enable information retrieval from a question-answering database and provide a human-like bot service. Given a specialized prompt, the chatbot undergoes information-retrieving and answer-generating processes to achieve its goal. We use accuracy and BERT scores to evaluate the quality of the answers, and the results of 0.8844 in accuracy and 0.8517 in BERT F1-score demonstrate the chatbot's capabilities in handling service requests as a replacement for human resources. Lastly, this paper highlights possibilities for further extensions that can be made to enhance the chatbot's reliability and improve the overall user experience.
AB - The surge in service requests at the Information Technology Service Center at the beginning of each semester poses a substantial strain on human resources, necessitating innovative solutions for efficient service management. With advancements in Natural Language Processing (NLP), AI chatbots have been introduced to address this challenge. This paper implements an AI chatbot utilizing OpenAI GPT-3.5 on the Information Technology Service Center website, discovering its capabilities of handling miscellaneous tasks. Our key contribution is to enable information retrieval from a question-answering database and provide a human-like bot service. Given a specialized prompt, the chatbot undergoes information-retrieving and answer-generating processes to achieve its goal. We use accuracy and BERT scores to evaluate the quality of the answers, and the results of 0.8844 in accuracy and 0.8517 in BERT F1-score demonstrate the chatbot's capabilities in handling service requests as a replacement for human resources. Lastly, this paper highlights possibilities for further extensions that can be made to enhance the chatbot's reliability and improve the overall user experience.
KW - BERT score
KW - GPT-3.5
KW - intelligence customer service
KW - OpenAI
KW - retrieval-based chatbot
UR - http://www.scopus.com/inward/record.url?scp=85206584984&partnerID=8YFLogxK
U2 - 10.1109/ICFTSS61109.2024.10691364
DO - 10.1109/ICFTSS61109.2024.10691364
M3 - Conference contribution
AN - SCOPUS:85206584984
T3 - ICFTSS 2024 - International Conference on Future Technologies for Smart Society
SP - 128
EP - 131
BT - ICFTSS 2024 - International Conference on Future Technologies for Smart Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Conference on Future Technologies for Smart Society, ICFTSS 2024
Y2 - 7 August 2024 through 8 August 2024
ER -