# YAC: Bridging Natural Language and Interactive Visual Exploration with Generative AI for Biomedical Data Discovery

arXiv: https://arxiv.org/abs/2509.19182 · HTML: https://mlsanity.com/paper/2509.19182/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 65 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 29 of 29)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models](https://mlsanity.com/paper/2303.02927/) (2023)
- [NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries](https://mlsanity.com/paper/2008.10723/) (2020)
- [Towards Natural Language Interfaces for Data Visualization: A Survey](https://mlsanity.com/paper/2109.03506/) (2021)
- [TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools](https://mlsanity.com/paper/2503.10970/) (2025)
- [Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks](https://mlsanity.com/paper/1804.03126/) (2018)
- [VisEval: A Benchmark for Data Visualization in the Era of Large Language Models](https://mlsanity.com/paper/2407.00981/) (2024)
- [DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing](https://mlsanity.com/paper/2401.10880/) (2024)
- [AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization](https://mlsanity.com/paper/2102.01330/) (2021)
- [Data Formulator: AI-powered Concept-driven Visualization Authoring](https://mlsanity.com/paper/2309.10094/) (2023)
- [Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements](https://mlsanity.com/paper/1907.09091/) (2019)
- [FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System](https://mlsanity.com/paper/1908.00681/) (2019)
- [Towards Natural Language-Based Visualization Authoring](https://mlsanity.com/paper/2208.10947/) (2022)
- [AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making](https://mlsanity.com/paper/2312.04494/) (2023)
- [Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the Way](https://mlsanity.com/paper/2408.16119/) (2024)
- [Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review](https://mlsanity.com/paper/2501.12557/) (2025)
- [KNOWNET: Guided Health Information Seeking from LLMs via Knowledge Graph Integration](https://mlsanity.com/paper/2407.13598/) (2024)
- [InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions](https://mlsanity.com/paper/2503.04110/) (2025)
- [Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education](https://mlsanity.com/paper/2407.20570/) (2024)
- [DracoGPT: Extracting Visualization Design Preferences from Large Language Models](https://mlsanity.com/paper/2408.06845/) (2024)
- [Sporthesia: Augmenting Sports Videos Using Natural Language](https://mlsanity.com/paper/2209.03434/) (2022)
- [PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets](https://mlsanity.com/paper/2407.16329/) (2024)
- [Sportify: Question Answering with Embedded Visualizations and Personified Narratives for Sports Video](https://mlsanity.com/paper/2408.05123/) (2024)
- [Exploring Multimodal Prompt for Visualization Authoring with Large Language Models](https://mlsanity.com/paper/2504.13700/) (2025)
- [A Natural-language-based Visual Query Approach of Uncertain Human Trajectories](https://mlsanity.com/paper/1908.00277/) (2019)
- [ReVISit 2: A Full Experiment Life Cycle User Study Framework](https://mlsanity.com/paper/2508.03876/) (2025)
- [Sentifiers: Interpreting Vague Intent Modifiers in Visual Analysis using Word Co-occurrence and Sentiment Analysis](https://mlsanity.com/paper/2009.12701/) (2020)
- [Technical Dimensions of Programming Systems](https://mlsanity.com/paper/2302.10003/) (2023)
- [Mixing Linters with GUIs: A Color Palette Design Probe](https://mlsanity.com/paper/2407.21285/) (2024)

## Built on by (most cited first, 1 of 1)

- [Agentic Authoring of Interactive Multiview Visualizations in Genomics](https://mlsanity.com/paper/2606.00370/) (2026)

## Abstract (arXiv)

Incorporating natural language input has the potential to improve the capabilities of biomedical data discovery interfaces. However, user interface elements and visualizations are still powerful tools for interacting with data. In our prototype system, YAC, Yet Another Chatbot, we integrate natural language and interactive visualizations. YAC uses a tool-calling multi-agent system to generate declarative output, which is interpreted to render linked interactive visualizations and apply data filters. We also include adjustment widgets, which allow users to directly modify the structured output. Structured text is also generated to clarify user intent, notify users of system boundaries, and explain aspects of the data with live data element links. We conducted a user study with domain experts to surface areas where YAC can be improved. Furthermore we reflect on the capabilities and design of this system with an analysis of its technical dimensions.