A Low-Code Approach for the Automatic Personalization of Conversational Agents

Abstract

The rise of Large Language Models (LLMs) has increased the demand for Conversational Agents (CAs) capable of understanding human conversations as part of web applications. While traditional CAs consist of deterministic states, LLMs enhance their capabilities to handle open conversations, handling arbitrary requests. Numerous tools exist that allow non-technical users to create such CAs. Yet, the creation of personalized CAs able to adapt to the profile of end-users to offer an optimal user experience remains in the hands of experienced developers implementing ad-hoc personalizations. In this work, we propose a pipeline that follows a low-code/no-code approach to facilitate the modeling and generation of personalized CAs. A pilot user study was performed to get preliminary results on perceived usability and usefulness and the full pipeline has been implemented on top of an open-source low-code platform.