Towards a unified user modeling language for engineering human centered AI systems
- URL: http://arxiv.org/abs/2505.24697v1
- Date: Fri, 30 May 2025 15:20:15 GMT
- Title: Towards a unified user modeling language for engineering human centered AI systems
- Authors: Aaron Conrardy, Alfredo Capozucca, Jordi Cabot,
- Abstract summary: A new wave of intelligent user interfaces, such as AI-based conversational agents, has the potential to enable such personalization.<n>This paper presents the concepts of a unified user modeling language, aimed to combine previous approaches in a single proposal.<n>A proof of concept has been developed that leverages user profiles modeled using our language to automatically adapt a conversational agent.
- Score: 1.7450893625541586
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In today's digital society, personalization has become a crucial aspect of software applications, significantly impacting user experience and engagement. A new wave of intelligent user interfaces, such as AI-based conversational agents, has the potential to enable such personalization beyond what other types of interfaces could offer in the past. Personalization requires the ability to specify a complete user profile, covering as many dimensions as possible, such as potential accessibility constraints, interaction preferences, and even hobbies. In this sense, this paper presents the concepts of a unified user modeling language, aimed to combine previous approaches in a single proposal. Additionally, a proof of concept has been developed that leverages user profiles modeled using our language to automatically adapt a conversational agent.
Related papers
- SynthesizeMe! Inducing Persona-Guided Prompts for Personalized Reward Models in LLMs [54.45812414534713]
We introduce SynthesizeMe, an approach to inducing synthetic user personas from user interactions for personalized reward modeling.<n>We show that using SynthesizeMe induced prompts improves personalized prompts by 4.4% on Arena.
arXiv Detail & Related papers (2025-06-05T21:23:16Z) - PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data [76.21047984886273]
Personalization is critical in AI assistants, particularly in the context of private AI models that work with individual users.<n>Due to the sensitive nature of such data, there are no publicly available datasets that allow us to assess an AI model's ability to understand users.<n>We introduce a synthetic data generation pipeline that creates diverse, realistic user profiles and private documents simulating human activities.
arXiv Detail & Related papers (2025-02-28T00:43:35Z) - User Modeling in Model-Driven Engineering: A Systematic Literature Review [1.7450893625541586]
We conduct a systematic literature review to analyze existing proposals for user modeling in model-driven engineering (MDE) approaches.<n>The results showcase that there is a lack of a unified and complete user modeling perspective.<n>This limits the implementation of richer user interfaces able to better support the user-specific needs.
arXiv Detail & Related papers (2024-12-20T13:19:57Z) - Generative AI in Multimodal User Interfaces: Trends, Challenges, and Cross-Platform Adaptability [0.0]
Generative AI emerges as a key driver in reshaping user interfaces.
This paper explores the integration of generative AI in modern user interfaces.
It focuses on multimodal interaction, cross-platform adaptability and dynamic personalization.
arXiv Detail & Related papers (2024-11-15T14:49:58Z) - Survey of User Interface Design and Interaction Techniques in Generative AI Applications [79.55963742878684]
We aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike.
We also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.
arXiv Detail & Related papers (2024-10-28T23:10:06Z) - PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization [9.594958534074074]
We introduce the PEFT-U Benchmark: a new dataset for building and evaluating NLP models for user personalization.
We explore the challenge of efficiently personalizing LLMs to accommodate user-specific preferences in the context of diverse user-centered tasks.
arXiv Detail & Related papers (2024-07-25T14:36:18Z) - Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond [87.1712108247199]
Our goal is to establish a Unified paradigm for Multi-modal Personalization systems (UniMP)
We develop a generic and personalization generative framework, that can handle a wide range of personalized needs.
Our methodology enhances the capabilities of foundational language models for personalized tasks.
arXiv Detail & Related papers (2024-03-15T20:21:31Z) - Personalized Language Modeling from Personalized Human Feedback [45.16986573937782]
Personalized large language models (LLMs) are designed to tailor responses to individual user preferences.<n>We propose Personalized-RLHF, an efficient framework that utilizes a lightweight user model to capture individual user preferences.<n>We show that personalized LLMs trained using P-RLHF generate responses that are more closely aligned with individual user preferences.
arXiv Detail & Related papers (2024-02-06T04:18:58Z) - Prompt-to-OS (P2OS): Revolutionizing Operating Systems and
Human-Computer Interaction with Integrated AI Generative Models [10.892991111926573]
We present a paradigm for human-computer interaction that revolutionizes the traditional notion of an operating system.
Within this innovative framework, user requests issued to the machine are handled by an interconnected ecosystem of generative AI models.
This visionary concept raises significant challenges, including privacy, security, trustability, and the ethical use of generative models.
arXiv Detail & Related papers (2023-10-07T17:16:34Z) - When Large Language Models Meet Personalization: Perspectives of
Challenges and Opportunities [60.5609416496429]
The capability of large language models has been dramatically improved.
Such a major leap-forward in general AI capacity will change the pattern of how personalization is conducted.
By leveraging large language models as general-purpose interface, personalization systems may compile user requests into plans.
arXiv Detail & Related papers (2023-07-31T02:48:56Z) - Interactive Text Generation [75.23894005664533]
We introduce a new Interactive Text Generation task that allows training generation models interactively without the costs of involving real users.
We train our interactive models using Imitation Learning, and our experiments against competitive non-interactive generation models show that models trained interactively are superior to their non-interactive counterparts.
arXiv Detail & Related papers (2023-03-02T01:57:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.