Unearthing Large Scale Domain-Specific Knowledge from Public Corpora
Keywords: domain-specific knowledge, data collection, large language model
Abstract: Large language models (LLMs) have demonstrated remarkable potential in various tasks, however, there remains a significant lack of open-source models and data for specific domains. Previous work has primarily focused on manually specifying resources and collecting high-quality data for specific domains, which is extremely time-consuming and labor-intensive. To address this limitation, we introduce large models into the data collection pipeline to guide the generation of domain-specific information and retrieve relevant data from Common Crawl (CC), a large public corpus. We refer to this approach as Retrieve-from-CC. It not only collects data related to domain-specific knowledge but also mines the data containing potential reasoning procedures from the public corpus. By applying this method, we have collected a knowledge domain-related dataset named Retrieve-Pile, which covers four main domains, including the sciences, humanities, and other categories. Through the analysis of Retrieve-Pile, Retrieve-from-CC can effectively retrieve relevant data from the covered knowledge domains and significantly improve the performance in tests of mathematical and knowledge-related reasoning abilities.
Supplementary Material: pdf
Primary Area: datasets and benchmarks
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Submission Number: 6586
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