# Conversational AI Threads for Visualizing Multidimensional Datasets

arXiv: https://arxiv.org/abs/2311.05590 · HTML: https://mlsanity.com/paper/2311.05590/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 74 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 22 of 22)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Faith and Fate: Limits of Transformers on Compositionality](https://mlsanity.com/paper/2305.18654/) (2023)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Synchromesh: Reliable code generation from pre-trained language models](https://mlsanity.com/paper/2201.11227/) (2022)
- [A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level](https://mlsanity.com/paper/2112.15594/) (2021)
- [Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling](https://mlsanity.com/paper/1906.07241/) (2019)
- [Is GPT-4 a Good Data Analyst?](https://mlsanity.com/paper/2305.15038/) (2023)
- [Towards Natural Language Interfaces for Data Visualization: A Survey](https://mlsanity.com/paper/2109.03506/) (2021)
- [Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizations](https://mlsanity.com/paper/2110.00680/) (2021)
- [FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System](https://mlsanity.com/paper/1908.00681/) (2019)
- [Iris: A Conversational Agent for Complex Tasks](https://mlsanity.com/paper/1707.05015/) (2017)
- [How do you Converse with an Analytical Chatbot? Revisiting Gricean Maxims for Designing Analytical Conversational Behavior](https://mlsanity.com/paper/2203.08420/) (2022)
- [Snowy: Recommending Utterances for Conversational Visual Analysis](https://mlsanity.com/paper/2110.04323/) (2021)
- [Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows](https://mlsanity.com/paper/2105.00121/) (2021)
- [Talk2Data: A Natural Language Interface for Exploratory Visual Analysis via Question Decomposition](https://mlsanity.com/paper/2107.14420/) (2021)
- [Chart2Vec: A Universal Embedding of Context-Aware Visualizations](https://mlsanity.com/paper/2306.08304/) (2023)
- [Code Code Evolution: Understanding How People Change Data Science Notebooks Over Time](https://mlsanity.com/paper/2209.02851/) (2022)
- [Urania: Visualizing Data Analysis Pipelines for Natural Language-Based Data Exploration](https://mlsanity.com/paper/2306.07760/) (2023)

## Built on by (most cited first, 8 of 8)

- [VisEval: A Benchmark for Data Visualization in the Era of Large Language Models](https://mlsanity.com/paper/2407.00981/) (2024)
- [Multimodal DeepResearcher: Generating Text-Chart Interleaved Reports From Scratch with Agentic Framework](https://mlsanity.com/paper/2506.02454/) (2025)
- [Vi(E)va LLM! A Conceptual Stack for Evaluating and Interpreting Generative AI-based Visualizations](https://mlsanity.com/paper/2402.02167/) (2024)
- [OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models](https://mlsanity.com/paper/2508.21061/) (2025)
- [Data Analysis in the Era of Generative AI](https://mlsanity.com/paper/2409.18475/) (2024)
- [ReSpark: Leveraging Previous Data Reports as References to Generate New Reports with LLMs](https://mlsanity.com/paper/2502.02329/) (2025)
- [Augmenting a Large Language Model with a Combination of Text and Visual Data for Conversational Visualization of Global Geospatial Data](https://mlsanity.com/paper/2501.09521/) (2025)
- [A Review on Large Language Models for Visual Analytics](https://mlsanity.com/paper/2503.15176/) (2025)

## Abstract (arXiv)

Generative Large Language Models (LLMs) show potential in data analysis, yet their full capabilities remain uncharted. Our work explores the capabilities of LLMs for creating and refining visualizations via conversational interfaces. We used an LLM to conduct a re-analysis of a prior Wizard-of-Oz study examining the use of chatbots for conducting visual analysis. We surfaced the strengths and weaknesses of LLM-driven analytic chatbots, finding that they fell short in supporting progressive visualization refinements. From these findings, we developed AI Threads, a multi-threaded analytic chatbot that enables analysts to proactively manage conversational context and improve the efficacy of its outputs. We evaluate its usability through a crowdsourced study (n=40) and in-depth interviews with expert analysts (n=10). We further demonstrate the capabilities of AI Threads on a dataset outside the LLM's training corpus. Our findings show the potential of LLMs while also surfacing challenges and fruitful avenues for future research.