# AlignUI: A Method for Designing LLM-Generated UIs Aligned with User Preferences

arXiv: https://arxiv.org/abs/2601.17614 · HTML: https://mlsanity.com/paper/2601.17614/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and multimodal model alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 14 of its 65 references that are in the map.
- First posted on arXiv 24 Jan 2026.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Diffusion and multimodal model alignment](https://mlsanity.com/alignment/diffusion-and-multimodal-model-alignment/)

## Builds on (most cited first, 14 of 14)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Evaluating Large Language Models at Evaluating Instruction Following](https://mlsanity.com/paper/2310.07641/) (2023)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning](https://mlsanity.com/paper/2108.03353/) (2021)
- [FIRST: A Framework for Optimizing Information Quality in Mobile Crowdsensing Systems](https://mlsanity.com/paper/1804.11147/) (2018)
- [DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing](https://mlsanity.com/paper/2401.10880/) (2024)
- [Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps](https://mlsanity.com/paper/1802.02312/) (2018)
- [Generative and Malleable User Interfaces with Generative and Evolving Task-Driven Data Model](https://mlsanity.com/paper/2503.04084/) (2025)
- [Leveraging Multimodal LLM for Inspirational User Interface Search](https://mlsanity.com/paper/2501.17799/) (2025)
- [BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks](https://mlsanity.com/paper/2404.07387/) (2024)
- [The GenUI Study: Exploring the Design of Generative UI Tools to Support UX Practitioners and Beyond](https://mlsanity.com/paper/2501.13145/) (2025)
- [TaskLens: Generating Task-Conditioned Scaffolded Interfaces for Learning Professional Creative Software](https://mlsanity.com/paper/2511.23379/) (2025)

## Built on by (most cited first, 2 of 2)

- [Efficient Personalization of Generative User Interfaces](https://mlsanity.com/paper/2604.09876/) (2026)
- [SchemaGUI: A Schema-Driven Benchmark for Controllable GUI Generation Evaluation](https://mlsanity.com/paper/2608.22390/) (2026)

## Abstract (arXiv)

Designing user interfaces that align with user preferences is a time-consuming process, which requires iterative cycles of prototyping, user testing, and refinement. Recent advancements in LLM-based UI generation have enabled efficient UI generation to assist the UI design process. We introduce AlignUI, a method that aligns LLM-generated UIs with user tasks and preferences by using a user preference dataset to guide the LLM's reasoning process. The dataset was crowdsourced from 50 general users (the target users of generated UIs) and contained 720 UI control preferences on eight image-editing tasks. We evaluated AlignUI by generating UIs for six unseen tasks and conducting a user study with 72 additional general users. The results showed that the generated UIs closely align with multiple dimensions of user preferences. We conclude by discussing the applicability of our method to support user-aligned UI design for multiple task domains and user groups, as well as personalized user needs.