Zero-Shot and Few-Shot Learning for Dynamic Knowledge Base Updates in Chatbots

Abstract

Chatbots are increasingly dependent on dynamic knowledge bases to generate on-topic, context-aware responses in real-time conversations. Historically, these knowledge bases must be updated from large annotated datasets and constant retraining by hand, which poses scalability and responsiveness issues in fast-changing domains. Recent breakthroughs in zero-shot and few-shot learning have been promising to enable models to generalize to new tasks or classes with limited or no additional labeled data. It remains an underresearched area, however, to apply such approaches to dynamic updates in chatbot knowledge bases. Current studies are primarily on static knowledge bases or require extensive fine-tuning, and thus they do not fit to solve the problem related to continual updates and domain change. This research fills a critical gap in integrating zero-shot and few-shot learning methods to allow chatbots to update their knowledge bases autonomously and efficiently with little supervision. This research introduces a new framework that leverages zero-shot and few-shot learning methods to dynamically learn, extract, and add new knowledge to chatbot systems. With the integration of transfer learning and context awareness, the framework seeks to alleviate reliance on large labeled datasets and manual tuning. Our method encourages more adaptive, scalable, and resilient chatbot behavior in changing environments. We test the framework on multiple real-world conversational datasets and show substantial improvements in update speed, accuracy, and generalization over traditional approaches. This research makes a contribution to the field of chatbot knowledge management by bridging the gap between new learning paradigms and requirements for real-world deployment, ultimately resulting in more intelligent and self-sustaining conversational agents.