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arxiv: 2409.02691 · v1 · pith:WMM75GCP · submitted 2024-09-04 · cs.HC · cs.AI

LLM-Assisted Visual Analytics: Opportunities and Challenges

pith:WMM75GCPopen to challenge →

classification cs.HC cs.AI
keywords languagellmssystemsanalyticschallengescurrentespeciallygeneration
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We explore the integration of large language models (LLMs) into visual analytics (VA) systems to transform their capabilities through intuitive natural language interactions. We survey current research directions in this emerging field, examining how LLMs are integrated into data management, language interaction, visualisation generation, and language generation processes. We highlight the new possibilities that LLMs bring to VA, especially how they can change VA processes beyond the usual use cases. We especially highlight building new visualisation-language models, allowing access of a breadth of domain knowledge, multimodal interaction, and opportunities with guidance. Finally, we carefully consider the prominent challenges of using current LLMs in VA tasks. Our discussions in this paper aim to guide future researchers working on LLM-assisted VA systems and help them navigate common obstacles when developing these systems.

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Cited by 1 Pith paper

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  1. Exploring Agentic Visual Analytics: A Co-Evolutionary Framework of Roles and Workflows

    cs.DB 2026-04 unverdicted novelty 7.0

    A survey of 55 agentic VA systems proposes a co-evolutionary framework defining four agent roles (PLANNER, CREATOR, REVIEWER, CONTEXT MANAGER) mapped to visual analytics pipeline stages along with design guidelines.