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Nabin Khanal

dblp:387/5647 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0007-1972-7022ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Query processing and optimization · 33% Data models and query languages · 33% Information retrieval · 33%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Query processing and optimization
interactive data exploration
0.812024
FathomGPT: A natural language interface for interactively exploring ocean science data · UIST 2024
Data models and query languages
natural language interface
0.812024
FathomGPT: A natural language interface for interactively exploring ocean science data · UIST 2024

Methods — techniques the papers use, named apart from their topics

prompt modification · 1.5large language model · 1.5fine-tuning · 1.5ablation study · 1.5
YearPublicationVenuePosition
2024 FathomGPT: A natural language interface for interactively exploring ocean science data
abstract
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 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. We also present usage scenarios of interactive data exploration sessions and document feedback from ocean scientists and machine learning experts.
Nabin Khanal, Chun Meng Yu, Jui-Cheng Chiu, Anav Chaudhary, Kakani Katija, Angus G. Forbes
UIST1