Advances in Deep Learning for Meta-Analysis in AI-Driven Chatbots

Kyldo Jsowd

No amdqz, OSF Preprints from Center for Open Science

Abstract: This paper explores the recent advances in deep learning techniques for meta-analysis in AI-driven chatbots. Chatbots have become increasingly popular in various domains, offering intelligent conversational interfaces to interact with users. Meta-analysis, as a research methodology, allows for the systematic synthesis and analysis of findings from multiple studies. Deep learning has emerged as a powerful approach within AI, enabling chatbots to understand natural language, generate context-aware responses, and improve their performance over time. This paper reviews the advancements in deep learning techniques specifically applied to meta-analysis in the context of AI-driven chatbots. It examines the utilization of deep neural networks, recurrent neural networks, and attention mechanisms in meta-analysis tasks. The paper also discusses the challenges and future research directions in leveraging deep learning for meta-analysis in AI-driven chatbots.

Date: 2023-07-16
New Economics Papers: this item is included in nep-ain, nep-cmp and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:amdqz

DOI: 10.31219/osf.io/amdqz

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