Browsing by Author "Van Eeden, Christiaan"

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    Adaptive conversational systems harnessing human expertise in modern chatbots
    (Stellenbosch : Stellenbosch University, 2024-03) Van Eeden, Christiaan; Du Preez, Johan; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: Chatbots are a rapidly evolving technology integral to the automation of customer service. Traditionally designed around complex if-then-else statements, the technology has seen a paradigm shift towards deep learning techniques for improved flexibility and user interaction. This research proposes a novel approach for training chatbots to learn more effectively from human support agents within automated customer service. Capitalising on the extensive data generated by these agents, we develop a framework to train chatbot models to emulate human-like responses, encapsulating nuances of unique language and individual mannerisms. Using state-of-the-art machine learning and natural language processing techniques, we train these models, achieving more contextually appropriate and authentic responses that accommodate the subtle complexities of human interaction. While commercial large language models (LLMs) like ChatGPT have demonstrated proficiency in customer service automation, they present limitations that could hinder their practical use. Such models, being proprietary, are not available for modification, impeding a company’s capacity to customise them to their specific needs. The costs, both financial and technical, associated with training a bespoke LLM can also be prohibitive for many organisations. Furthermore, these models, with their billions of parameters, require substantial hardware resources and may struggle to manage high-volume, swift interactions typical in a customer service environment. Additionally, due to their general-purpose nature, these models can occasionally produce unpredictable or undesirable responses as they lack specific domain knowledge. Our proposed model, developed on the more manageable GPT-2, offers a tailored, cost-effective, and adaptive solution to these challenges. Although our research is limited in its scope, the findings indicate an improvement in the usability and effectiveness of chatbots trained with our proposed method. This study contributes to the broader field of AI-driven customer service by augmenting the development of more sophisticated and user-friendly chatbot systems.