Data Mixing Made Efficient: A Bivariate Scaling Law for Language Model
  Pretraining
Large language models exhibit exceptional generalization capabilities,
primarily attributed to the utilization of diversely sourced data. However,
conventional practices in integrating this diverse data heavily rely on
heuristic schemes, lacking theoretical guidance. This research tackles these
limitations by investigating strategies based on low-cost proxies for data
mixtures, with the aim of streamlining data curation to enhance training
efficiency. Specifically, we propose a unified scaling law, termed BiMix, which
accurately models the bivariate scaling behaviors of both data quantity and
mixing proportions. We conduct systematic experiments and provide empirical
evidence for the predictive power and fundamental principles of BiMix. Notably,
our findings reveal that entropy-driven training-free data mixtures can achieve
comparable or even better performance than more resource-intensive methods. We
hope that our quantitative insights can shed light on further judicious
research and development in cost-effective language modeling.
6653ec0ce6ddaa4f893ebc2f
Ce Ge
6653ec0ce6ddaa4f893ebc30
Zhijian Ma
6653ec0ce6ddaa4f893ebc31
Daoyuan Chen
admin_assigned
2024-05-27T09:59:44.181Z
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/avatars/7b1ec5b75398c8434ccd43cdce7fee53.svg
Daoyuan Chen
user
yxdyc
6653ec0ce6ddaa4f893ebc32
Yaliang Li
6653ec0ce6ddaa4f893ebc33
Bolin Ding
Data Mixing Made Efficient: A Bivariate Scaling Law for Language Model
  Pretraining
Large language models exhibit exceptional generalization capabilities,
primarily attributed to the utilization of diversely sourced data. However,
conventional practices in integrating this diverse data heavily rely on
heuristic schemes, lacking theoretical guidance. This research tackles these
limitations by investigating strategies based on low-cost proxies for data
mixtures, with the aim of streamlining data curation to enhance training
efficiency. Specifically, we propose a unified scaling law, termed BiMix, which
accurately models the bivariate scaling behaviors of both data quantity and
mixing proportions. We conduct systematic experiments and provide empirical
evidence for the predictive power and fundamental principles of BiMix. Notably,
our findings reveal that entropy-driven training-free data mixtures can achieve
comparable or even better performance than more resource-intensive methods. We
hope that our quantitative insights can shed light on further judicious
research and development in cost-effective language modeling.
Research proposes BiMix scalability law to model bivariate data scaling behaviors in language models, leading to efficient data curation and improved performance.
large language models generalization capabilities data mixtures scaling law BiMix entropy-driven training-free data mixtures language modeling