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UltraX framework refines LLM pre-training data with adaptive programmatic editing

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced UltraX, a novel framework designed to refine large-scale pre-training data for large-language models (LLMs). This system addresses the diminishing returns from simply increasing data volume by focusing on data quality through adaptive programmatic editing. UltraX enhances data utilization by enabling fine-grained instance-level editing, including insertion, deletion, and modification, and employs a reliable program-supervision generation pipeline. Experiments indicate that UltraX improves data efficiency and refinement reliability, achieving high performance with fewer training tokens compared to existing methods. AI

IMPACT Enhances data efficiency and reliability for LLM training, potentially leading to better model performance with less data.

RANK_REASON The cluster describes a research paper detailing a new method for refining large-language model training data.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

UltraX framework refines LLM pre-training data with adaptive programmatic editing

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The cluster describes a research paper detailing a new method for refining large-language model training data.
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91 days old
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Xinlong Zhao, Dongsheng Liu, Hengyu Zhao, Zixuan Fu, Zheng Wang, Jie Cai, Jie Zhou, Qiang Ma, Xuanhe Zhou, Xu Han, Yudong Wang, Zhiyuan Liu ·

    UltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic Editing

    arXiv:2607.08646v1 Announce Type: cross Abstract: As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish. Consequently, improving Large Language Models (LLMs) now depends less on data expansion and more on higher-quality data util…

  2. arXiv cs.AI TIER_1 English(EN) · Zhiyuan Liu ·

    UltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic Editing

    As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish. Consequently, improving Large Language Models (LLMs) now depends less on data expansion and more on higher-quality data utilization. However, in the context of large-scale co…

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