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New law quantifies tokenization needs for billion-scale user data

By PulseAugur Editorial · [3 sources] ·

Researchers have proposed a "User Behavioral Densing Law" to quantify the relationship between data scale and tokenization capacity in large-scale user representation learning. A pilot study using a billion-scale Alipay dataset revealed a bottleneck in raw data scaling, which tokenization can mitigate. The proposed law identifies an approximately linear relationship between the logarithms of minimum sufficient tokenization capacity and input data size, offering practical guidance for selecting tokenization configurations. Additionally, an adaptive variable-length tokenization method called ALGN was developed, which demonstrated superior performance over existing baselines across various data sources and tasks. AI

IMPACT Provides practical guidance for optimizing tokenization in large-scale user representation learning, potentially improving efficiency and performance.

RANK_REASON The cluster contains a research paper detailing a new theoretical law and method for user representation learning.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New law quantifies tokenization needs for billion-scale user data

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The cluster contains a research paper detailing a new theoretical law and method for user representation learning.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Bin Dou, Junru Zhang, Zhaoyi Yuan, Wuliang Huang, Letian Gong, Baokun Wang, Huan Li, Yu Cheng, Weiqiang Wang ·

    Towards a Densing Law for User Representation Learning at Billion-Scale Capacity

    arXiv:2608.23392v1 Announce Type: cross Abstract: User representation learning in real-world industrial scenarios is commonly scaled by increasing user amount, behavioral sequence length and model size. However, existing methods face two challenges: (i) Bottleneck for raw data sc…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Weiqiang Wang ·

    Towards a Densing Law for User Representation Learning at Billion-Scale Capacity

    User representation learning in real-world industrial scenarios is commonly scaled by increasing user amount, behavioral sequence length and model size. However, existing methods face two challenges: (i) Bottleneck for raw data scaling at billion-scale capacity, as performance ex…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Towards a Densing Law for User Representation Learning at Billion-Scale Capacity

    The study proposes a scaling law linking data size to tokenization capacity for user behavior modeling and introduces an adaptive tokenization method to improve efficiency.

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