Leveraging LLMs to Create Content Corpora for Niche Domains
This addresses data curation challenges for domain-specific applications, such as habit formation in behavior education, though it is incremental as it applies existing LLM methods to a new domain.
The paper tackled the problem of constructing specialized content corpora from unstructured web sources by introducing a streamlined approach using Large Language Models (LLMs) for efficient data curation, resulting in the extraction of 3,531 unique 30-day challenges from over 15,000 webpages and a user satisfaction score of 4.3 out of 5.
Constructing specialized content corpora from vast, unstructured web sources for domain-specific applications poses substantial data curation challenges. In this paper, we introduce a streamlined approach for generating high-quality, domain-specific corpora by efficiently acquiring, filtering, structuring, and cleaning web-based data. We showcase how Large Language Models (LLMs) can be leveraged to address complex data curation at scale, and propose a strategical framework incorporating LLM-enhanced techniques for structured content extraction and semantic deduplication. We validate our approach in the behavior education domain through its integration into 30 Day Me, a habit formation application. Our data pipeline, named 30DayGen, enabled the extraction and synthesis of 3,531 unique 30-day challenges from over 15K webpages. A user survey reports a satisfaction score of 4.3 out of 5, with 91% of respondents indicating willingness to use the curated content for their habit-formation goals.