Pith. sign in

REVIEW 3 cited by

LLM-Assisted Visual Analytics: Opportunities and Challenges

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

classification cs.HCcs.AI
keywords languagellmssystemsanalyticschallengescurrentespeciallygeneration
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ManiScope: LLM-Assisted Visual Analytics of Cryptocurrency Manipulation Risk

    cs.HC 2026-07 conditional novelty 6.0 of 10

    An LLM co-analyst plus coordinated crypto views can reduce manual evidence-seeking and organize manipulation-risk findings around user hypotheses in a 12-person practitioner study.

  2. Qualitative Study for LLM-assisted Design Study Process: Strategies, Challenges, and Roles

    cs.HC 2025-07 conditional novelty 6.0 of 10

    Through interviews with 30 researchers, the paper identifies four roles that LLMs play in visualization design studies and maps them onto the nine-stage design study process.

  3. GOBench: Benchmarking Geometric Optics Generation and Understanding of MLLMs

    cs.CV 2025-06 conditional novelty 6.0 of 10

    GOBench measures how well multimodal AI models generate and understand geometric optics, finding that even top models make frequent physical errors.

Pith tools