Enhancing Chatbot Systems Through Meta-Analysis Integration: a Comprehensive Investigation

EasyChair Preprint 12483

Abstract

This study explores the augmentation of chatbot systems through the integration of meta-analysis techniques, aiming to significantly improve their overall performance. Meta-analysis, a statistical method for synthesizing findings from multiple studies, is leveraged to distill and combine insights derived from various machine learning-powered chatbot implementations. The comprehensive investigation encompasses an in-depth examination of diverse datasets, model architectures, and training methodologies. Through a systematic approach, we evaluate the impact of meta-analysis on enhancing the chatbot's conversational abilities, adaptability to user inputs, and overall effectiveness. Our findings reveal compelling evidence of the positive influence of meta-analysis integration, shedding light on the strengths and limitations of different machine learning paradigms. We present quantitative assessments and qualitative analyses, providing valuable insights for practitioners and researchers in the field of chatbot development. The results underscore the potential for meta-analysis as a strategic tool in refining and advancing the capabilities of machine learning-powered chatbot systems, offering a promising avenue for further exploration and optimization in the quest for more proficient conversational agents.

Keyphrases: Chatbot Systems, Meta-Analysis Integration, Performance enhancement, conversational agents, machine learning

@booklet{EasyChair:12483,
  author    = {Haney Zaki},
  title     = {Enhancing Chatbot Systems Through Meta-Analysis Integration: a Comprehensive Investigation},
  howpublished = {EasyChair Preprint 12483},
  year      = {EasyChair, 2024}}