arXiv:2606.11499v1 Announce Type: new Abstract: The performance of modern language models depends critically on pretraining data composition. Yet existing data selection methods rely on auxiliary classifiers for document scoring or mixture optimization, adding computational overhead and dependence on labeled data. We propose WebGraphMix, a lightweight data selection framework that computes structural centrality scores over the Common Crawl host-level web graph and uses them to vary the proportion of central versus peripheral documents in the pretraining mixture. We hypothesize that central hos
The paper addresses a critical current challenge in large language model development: effective pretraining data selection, which is becoming even more vital as models scale and data quality becomes a binding constraint.
Optimizing pretraining data composition can significantly enhance AI model performance and efficiency, reducing computational costs and dependence on exhaustive data labeling, which is key for competitive AI development.
This research introduces a novel, lightweight method for data selection that uses structural centrality scores from web graphs, potentially shifting data curation strategies away from expensive, label-dependent methods.
- · AI developers
- · Cloud infrastructure providers
- · Researchers in NLP
- · Companies with large unstructured datasets
- · Companies specializing in manual data labeling
- · Less efficient data curation methods
More efficient and performant language models can be developed with reduced resource expenditure.
The cost of developing advanced AI models may decrease, lowering barriers to entry and accelerating innovation across various sectors.
Improved AI models could lead to more accurate information retrieval and a better understanding of web content, potentially influencing information ecosystems.
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