REVIEW 8 cited by
LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models
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
Signed reviews
read the original abstract
Systems that support users in the automatic creation of visualizations must address several subtasks - understand the semantics of data, enumerate relevant visualization goals and generate visualization specifications. In this work, we pose visualization generation as a multi-stage generation problem and argue that well-orchestrated pipelines based on large language models (LLMs) such as ChatGPT/GPT-4 and image generation models (IGMs) are suitable to addressing these tasks. We present LIDA, a novel tool for generating grammar-agnostic visualizations and infographics. LIDA comprises of 4 modules - A SUMMARIZER that converts data into a rich but compact natural language summary, a GOAL EXPLORER that enumerates visualization goals given the data, a VISGENERATOR that generates, refines, executes and filters visualization code and an INFOGRAPHER module that yields data-faithful stylized graphics using IGMs. LIDA provides a python api, and a hybrid user interface (direct manipulation and multilingual natural language) for interactive chart, infographics and data story generation. Learn more about the project here - https://microsoft.github.io/lida/
Forward citations
Cited by 8 Pith papers
-
ProactiveVA: Proactive Visual Analytics with LLM-Based UI Agent
An LLM-based UI agent monitors visual analytics interactions, detects when users struggle, infers their intent, and proactively provides context-aware guidance.
-
AuraGenome: An LLM-Powered Framework for On-the-Fly Reusable and Scalable Circular Genome Visualizations
AuraGenome combines LLM agents, D3.js code templates, and a visual interface to generate, refine, and reuse circular genome visualizations from natural language.
-
Exploring Multimodal Prompt for Visualization Authoring with Large Language Models
VisPilot adds sketches, annotations, and direct manipulation to text prompts for LLM-driven chart creation, and a 10-participant study reports higher accuracy with equal speed versus text-only prompting.
-
Explainable XR: Understanding User Behaviors of XR Environments using LLM-assisted Analytics Framework
Explainable XR provides a unified, action-centric recording and visualization framework with LLM-generated insights for analyzing user behavior across AR, VR, and MR.
-
Data-to-Dashboard: Multi-Agent LLM Framework for Insightful Visualization in Enterprise Analytics
A multi-agent LLM system that detects the business domain of a raw dataset, generates domain-grounded insights, and renders them as charts, claims to beat single-prompt GPT-4o in insight quality.
-
FathomGPT: A Natural Language Interface for Interactively Exploring Ocean Science Data
FathomGPT is an open-source natural language interface for the FathomNet ocean image database, with ablations showing improved text-to-SQL accuracy through fine-tuning and prompt modification.
-
GeoPandas-AI: A Smart Class Bringing LLM as Stateful AI Code Assistant
A new open-source Python class, GeoDataFrameAI, adds a stateful LLM chat interface directly to GeoPandas data frames for geospatial code generation and analysis.
-
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future
A historical review that organizes multimodal explainability methods into four chronological eras and three explainability types, extending coverage to generative LLMs.
Discussion (0). Continue with ORCID to comment.