mlsanityData to 7 Oct 2026

GraphiMind: LLM-centric Interface for Information Graphics Design

arXiv 2401.13245 ↗First posted 24 Jan 2024cs.HCcited by 4

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Abstract (arXiv)

Information graphics are pivotal in effective information dissemination and storytelling. However, creating such graphics is extremely challenging for non-professionals, since the design process requires multifaceted skills and comprehensive knowledge. Thus, despite the many available authoring tools, a significant gap remains in enabling non-experts to produce compelling information graphics seamlessly, especially from scratch. Recent breakthroughs show that Large Language Models (LLMs), especially when tool-augmented, can autonomously engage with external tools, making them promising candidates for enabling innovative graphic design applications. In this work, we propose a LLM-centric interface with the agent GraphiMind for automatic generation, recommendation, and composition of information graphics design resources, based on user intent expressed through natural language. Our GraphiMind integrates a Textual Conversational Interface, powered by tool-augmented LLM, with a traditional Graphical Manipulation Interface, streamlining the entire design process from raw resource curation to composition and refinement. Extensive evaluations highlight our tool's proficiency in simplifying the design process, opening avenues for its use by non-professional users. Moreover, we spotlight the potential of LLMs in reshaping the domain of information graphics design, offering a blend of automation, versatility, and user-centric interactivity.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jan 2024.

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Citing papers per year: 2024: 1; 2025: 3.

4 papers.

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  1. 2025GraphicBench: A Planning Benchmark for Graphic Design with Language Agents
  2. 2026State of the Art of LLM-Enabled Interaction with Visualization ↗
  3. 2026ChartDesign: Towards LLM Designer of Data Visualization
  4. 2025PlotEdit: Natural Language-Driven Accessible Chart Editing in PDFs via Multimodal LLM Agents
  5. 2026Figures as Interfaces: Toward LLM-Native Artifacts for Scientific Discovery
  6. 2025CAL-RAG: Retrieval-Augmented Multi-Agent Generation for Content-Aware Layout Design
  7. 2025Automated Visualization Makeovers with LLMs
  8. 2025Structured Visualization Design Knowledge for Grounding Generative Reasoning and Situated Feedback