CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph
arXiv:2606.30175v1 Announce Type: new Abstract: The continuous evolution of large language models drives escalating demands on data scale and quality, and as different training stages impose increasingly tailored data requirements, systematic organization of high-quality corpora becomes indispensable. Existing corpus construction pipelines confine the resulting corpora to flat, undifferentiated document collections, universally lacking systematic knowledge organization. We present Cortex, to our knowledge the first framework that elevates web-scale corpus construction from flat document filter
The continuous evolution of large language models is driving escalating demands for higher quality, systematically organized web-scale data, which current corpus construction methods fail to provide.
Improved data organization and quality, as proposed by Cortex, directly addresses a critical bottleneck for advanced AI model training, potentially accelerating AI development and performance significantly.
The paradigm for constructing and organizing web-scale corpora would shift from flat, undifferentiated collections to structured, knowledge-based systems, enhancing training data utility and efficiency.
- · Large Language Model Developers
- · AI Research Institutions
- · Data Infrastructure Providers
- · Cloud Computing Platforms
- · Companies reliant on undifferentiated data
- · Legacy data management tools
More powerful and efficient large language models are developed due to higher quality training data.
The cost-effectiveness of training advanced AI models improves, lowering barriers for new entrants.
Enhanced AI capabilities lead to acceleration in various AI-driven applications and industries, potentially creating entirely new market segments.
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Read at arXiv cs.CL