SEED: Domain-Specific Data Curation With Large Language Models

arXiv:2310.00749v3 Announce Type: replace-cross
Abstract: Data curation tasks that prepare data for analytics are critical for turning data into actionable insights. However, due to the diverse requirements of applications in different domains, generic off-the-shelf tools are typically insufficient. As a result, data scientists often have to develop domain-specific solutions tailored to both the dataset and the task, e.g. writing domain-specific code or training machine learning models on a sufficient number of annotated examples. This process is notoriously difficult and time-consuming. …

abstract analytics applications arxiv cs.db cs.lg curation data data curation data scientists diverse domain domains however insights language language models large language large language models requirements scientists seed solutions tasks tools type