A Low-Code Approach for the Automatic Personalization of Conversational Agents
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
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.
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.
Builds on
6 of 26 references.
References · 6
- 2023Multilingual Machine Translation with Large Language Models: Empirical Results and Analysiscited by 160
- 2024CultureLLM: Incorporating Cultural Differences into Large Language Modelscited by 50
- 2024Building BESSER: an open-source low-code platformcited by 7
- 2024User Modeling in Model-Driven Engineering: A Systematic Literature Reviewcited by 1
- 2025Adaptive and Accessible User Interfaces for Seniors Through Model-Driven Engineeringcited by 1
- 2025Towards a unified user modeling language for engineering human centered AI systemscited by 1
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- 2025Human vs. Agent in Task-Oriented Conversations
- 2025PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants
- 2024Building Better AI Agents: A Provocation on the Utilisation of Persona in LLM-based Conversational Agents
- 2023Conversational Health Agents: A Personalized LLM-Powered Agent Framework
- 2024Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks
- 2026OP-Bench: Benchmarking Over-Personalization for Memory-Augmented Personalized Conversational Agents
- 2025Enabling Personalized Long-term Interactions in LLM-based Agents through Persistent Memory and User Profiles