Evaluating dialogue adaptability: A comparative study of self-feeding mechanisms in federated and centralized chatbot architectures

Abstract

Evaluating chatbot adaptability after deployment remains critical for ensuring ongoing relevance and user satisfaction. While previous research compared federated vs. traditional architectures for intent classification, the post-deployment adaptation capabilities of chatbots-particularly through self-feeding mechanisms-remain relatively unexplored. This paper evaluates self-feeding mechanisms in federated and centralized chatbot architectures, specifically investigating the impact of explicit and implicit user feedback on chatbot adaptability post-deployment. We empirically assess the effectiveness of these feedback loops in addressing data drift and improving intent classification accuracy over time. Through a comparative analysis, the study highlights distinct strengths and limitations in each approach, providing new insights into how chatbots can continuously enhance user experience and learning performance. Our findings emphasize the critical role of self-feeding mechanisms for sustainable chatbot operations, extending beyond initial training toward robust, ongoing performance improvements, complementing the literature on privacy-centric federated chatbot systems.