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New paper suggests host-aware crawling for better LLM pretraining data

By PulseAugur Editorial · [1 sources] ·

A new paper proposes a host-aware web crawling strategy to improve the quality of pretraining data for large language models (LLMs). The research indicates that a significant portion of data quality variation occurs between different hosts, and sampling a few pages from a host can effectively estimate the quality of its remaining content. The study suggests that prioritizing hosts with higher-scoring samples and crawling them more deeply could yield substantially more high-quality pages compared to current methods like random ordering or harmonic centrality. AI

IMPACT This research could lead to more efficient and effective data collection for LLM pretraining, potentially improving model performance and reducing costs.

RANK_REASON The cluster contains an academic paper detailing a new methodology for data collection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New paper suggests host-aware crawling for better LLM pretraining data

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The cluster contains an academic paper detailing a new methodology for data collection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Malte Ostendorff ·

    Host-Aware Web Crawling for LLM Pretraining Data

    arXiv:2610.13321v1 Announce Type: new Abstract: Web crawling is a major source of large language model (LLM) pretraining data, yet data-selection pipelines discard a large share of crawled pages. We investigate how a crawler should allocate its budget across and within hosts to c…

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