arXiv AI By Xuanyi Liu, Deyi Ji, Junyu Lu, Jing Wang, Qianxiong Xu, Xuhang Chen, Tianrun Chen, Siwei Ma

Seeing is Believing: Aligning Prompt Rewriting with Visual Anchors for Text-to-Image Generation

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arXiv:2606. 08492v1 Announce Type: cross Abstract: Despite the impressive capabilities of text-to-image (T2I) models, an intent-generation gap often persists due to the brevity and ambiguity of user prompts.

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arXiv AI
Sep 12

Exploring Multimodal Prompt for Visualization Authoring with Large Language Models

The paper investigates how large language models (LLMs) interpret ambiguous or incomplete text prompts for visualization authoring and introduces visual prompts as a complementary modality to improve precision. An empirical study informs the design of VisPilot, a system that allows users to create visualizations using text, sketches, and direct manipulation. A controlled user study and expert evaluation show that multimodal prompts help users convey spatial constraints, local references, and design preferences while maintaining task efficiency comparable to text-only prompting.

By Zhen Wen, Luoxuan Weng, Yinghao Tang, Runjin Zhang, Yuxin Liu, Bo Pan, Minfeng Zhu, Wei Chen