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Empowering Scientific Datasets with Large Language Models
DescriptionThe growing volume and complexity of scientific data pose significant challenges in data management, organization, and analysis. Our objective is to enhance the utilization of historical scientific datasets across various disciplines. To address this, we propose integrating large language models (LLMs) with databases to enable natural language queries, streamlined data retrieval, and analysis. Leveraging LangChain, our approach harnesses the capabilities of LLMs and complements them with data visualization and interpretation tools. Initial results using Llama 3.1 70B demonstrate an 88% success rate in searching and summarizing structured text and numerical data, showcasing the potential for LLM-powered tools to accelerate scientific discovery and innovation.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
TimeTuesday, 19 November 202412pm - 5pm EST
LocationB302-B305
Registration Categories
TP
XO/EX