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

arXiv: https://arxiv.org/abs/2605.02384 · HTML: https://mlsanity.com/paper/2605.02384/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 4 May 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 6 of 6)

- [Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis](https://mlsanity.com/paper/2304.04675/) (2023)
- [CultureLLM: Incorporating Cultural Differences into Large Language Models](https://mlsanity.com/paper/2402.10946/) (2024)
- [Building BESSER: an open-source low-code platform](https://mlsanity.com/paper/2405.13620/) (2024)
- [User Modeling in Model-Driven Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2412.15871/) (2024)
- [Adaptive and Accessible User Interfaces for Seniors Through Model-Driven Engineering](https://mlsanity.com/paper/2502.18828/) (2025)
- [Towards a unified user modeling language for engineering human centered AI systems](https://mlsanity.com/paper/2505.24697/) (2025)

## Abstract (arXiv)

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.