FathomGPT: A Natural Language Interface for Interactively Exploring Ocean Science Data
Authors
We introduce FathomGPT, an open source system for the interactive investigation of ocean science data via a natural language interface. FathomGPT was developed in close collaboration with marine scientists to enable researchers and ocean enthusiasts to explore and analyze the FathomNet image database. FathomGPT provides a custom information retrieval pipeline that leverages OpenAI’s large language models to enable: the creation of complex queries to retrieve images, taxonomic information, and scientific measurements; mapping common names and morphological features to scientific names; generating interactive charts on demand; and searching by image or specified patterns within an image. In designing FathomGPT, particular emphasis was placed on enhancing the user's experience by facilitating free-form exploration and optimizing response times. We present an architectural overview and implementation details of FathomGPT, along with a series of ablation studies that demonstrate the effectiveness of our approach to name resolution, fine tuning, and prompt modification. Additionally, we present usage scenarios of interactive data exploration sessions and document feedback from ocean scientists and machine learning experts.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 60%
Between Scripts and Applications: Computational Media for the Frontier of Nanoscience
CHI '20· Interactive Data Visualization +1
- 60%
Visualizing Examples of Deep Neural Networks at Scale
CHI '21· Human-LLM Collaboration +1
- 60%
Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data Work
CHI '23· Human-LLM Collaboration +1
- 60%
Decision rule elicitation for domain adaptation
IUI '21· Human-LLM Collaboration +1
- 60%
VibE: A Visual Analytics Workflow for Semantic Error Analysis of CVML Models at Subgroup Level
IUI '25· Human-LLM Collaboration +1
- 60%
Qlarify: Recursively Expandable Abstracts for Dynamic Information Retrieval over Scientific Papers
UIST '24· Human-LLM Collaboration +1
- 60%
AbstractExplorer: Leveraging Structure-Mapping Theory to Enhance Comparative Close Reading at Scale
UIST '25· Human-LLM Collaboration +1
Based on Jaccard similarity of research subtopics & professions (≥60%)