[Paper Review] Revolutionizing Customer Interactions: Insights and Challenges in Deploying ChatGPT and Generative Chatbots for FAQs

Feriel Khennouche, Youssef Elmir+3 more|arXiv (Cornell University)|Nov 16, 2023

AI in Service Interactions4 citations

TL;DR

This paper presents a comprehensive analysis of deploying ChatGPT and generative chatbots for FAQ systems, demonstrating their ability to enhance user interaction through advanced NLP techniques like intent classification and sentiment analysis. The study highlights improved response relevance and user experience, while identifying key challenges in ethical alignment, domain adaptation, and conversational coherence.

ABSTRACT

In the rapidly evolving domain of artificial intelligence, chatbots have emerged as a potent tool for various applications ranging from e-commerce to healthcare. This research delves into the intricacies of chatbot technology, from its foundational concepts to advanced generative models like ChatGPT. We present a comprehensive taxonomy of existing chatbot approaches, distinguishing between rule-based, retrieval-based, generative, and hybrid models. A specific emphasis is placed on ChatGPT, elucidating its merits for frequently asked questions (FAQs)-based chatbots, coupled with an exploration of associated Natural Language Processing (NLP) techniques such as named entity recognition, intent classification, and sentiment analysis. The paper further delves into the customization and fine-tuning of ChatGPT, its integration with knowledge bases, and the consequent challenges and ethical considerations that arise. Through real-world applications in domains such as online shopping, healthcare, and education, we underscore the transformative potential of chatbots. However, we also spotlight open challenges and suggest future research directions, emphasizing the need for optimizing conversational flow, advancing dialogue mechanics, improving domain adaptability, and enhancing ethical considerations. The research culminates in a call for further exploration in ensuring transparent, ethical, and user-centric chatbot systems.

Motivation & Objective

• To analyze the current state of chatbot technology, particularly focusing on generative models like ChatGPT for FAQ-based customer interactions.
• To identify the technical and ethical challenges in deploying large language models (LLMs) such as ChatGPT in real-world FAQ applications.
• To evaluate the integration of ChatGPT with knowledge bases and NLP techniques to improve response accuracy and contextual relevance.
• To examine the impact of user feedback and fine-tuning on model performance and long-term adaptability.
• To outline future research directions for enhancing conversational flow, domain adaptability, and ethical transparency in AI-driven chatbots.

Proposed method

• Developed a taxonomy of chatbot approaches, distinguishing rule-based, retrieval-based, generative, and hybrid models.
• Evaluated ChatGPT’s performance in FAQ scenarios using NLP techniques including intent classification, named entity recognition, and sentiment analysis.
• Proposed integration of ChatGPT with external knowledge bases to enhance factual accuracy and reduce hallucination.
• Explored fine-tuning and continual learning strategies using reinforcement learning and active sampling to incorporate user feedback.
• Applied cumulative learning techniques to enable incremental model updates without full retraining.
• Advocated for transparency mechanisms, such as explaining model updates and limitations, to improve user trust and system accountability.

Figure 1: General chatbot structure.

Experimental results

Research questions

• RQ1 How do generative chatbots like ChatGPT improve response quality and user experience in FAQ-based systems compared to traditional rule-based or retrieval-based models?
• RQ2 What are the key technical and ethical challenges in deploying ChatGPT for real-world FAQ applications across domains like healthcare, e-commerce, and education?
• RQ3 How can user feedback be effectively integrated into ChatGPT to enable continuous learning and model improvement without retraining?
• RQ4 What role do NLP techniques such as intent classification and sentiment analysis play in enhancing the performance of generative FAQ chatbots?
• RQ5 What future research directions are needed to improve conversational coherence, domain adaptability, and ethical alignment in LLM-powered chatbots?

Key findings

• ChatGPT significantly improves response relevance and contextual coherence in FAQ systems compared to traditional rule-based or retrieval-based models.
• Integration with external knowledge bases reduces hallucination and enhances factual accuracy in generated responses.
• Fine-tuning and feedback-based learning enable incremental model improvement without full retraining, supporting long-term adaptability.
• User feedback integration via active learning and preference learning enhances model performance and personalization over time.
• Ethical challenges such as bias, data privacy, and lack of transparency remain critical barriers, requiring systematic mitigation through bias testing and explainability mechanisms.
• Transparency features—such as signaling knowledge limits and explaining model updates—improve user trust and responsible AI interaction.

Figure 2: Progression of the number of works related to FAQ Chatbot over the last 10 years.