Predictive Data Selection: The Data That Predicts Is the Data That
  Teaches
Language model pretraining involves training on extensive corpora, where data
quality plays a pivotal role. In this work, we aim to directly estimate the
contribution of data during pretraining and select pretraining data in an
efficient manner. Specifically, we draw inspiration from recent findings
showing that compression efficiency (i.e., the normalized loss) of diverse
models on certain text correlates strongly with their downstream performance,
when the text domain aligns with the downstream benchmark (Huang et al., 2024).
Building on this observation, we hypothesize that data on which model losses
are predictive of downstream abilities also contribute effectively to learning.
To leverage this insight, we introduce data selection based on data's
Predictive strength (Preselect), a lightweight and efficient data selection
method that requires training and deploying only a fastText-based scorer.
Through comprehensive experiments with 1B and 3B parameter models, we
demonstrate that models trained on 30B tokens selected with PreSelect surpasses
the performance of a vanilla baseline trained on 300B tokens, achieving a 10x
reduction in compute requirements. Furthermore, PreSelect significantly
outperforms other competitive data selection baselines, such as DCLM and
FineWeb-Edu on a scale of 3B models trained on 100B tokens. We open-source our
trained data selection scorer along with the curated datasets at
https://github.com/hkust-nlp/PreSelect.
67c66382e5394bda7cbd03f9
Kashun Shum
claimed_verified
2025-03-04T08:51:25.484Z
641c9662043963b1c0a1df52
https://cdn-avatars.huggingface.co/v1/production/uploads/641c9662043963b1c0a1df52/L1o85EHztv_xP9r6ppljf.jpeg
KaShun SHUM
user
ksshumab
67c66382e5394bda7cbd03fa
Yuzhen Huang
claimed_verified
2025-03-04T08:51:23.329Z
6462def82a83863b97c0611e
/avatars/c03e9cc7d75b0266fcc56ecb6ee62148.svg
Yuzhen Huang
user
yuzhen17
67c66382e5394bda7cbd03fb
Hongjian Zou
67c66382e5394bda7cbd03fc
Ding Qi
67c66382e5394bda7cbd03fd
Yixuan Liao
67c66382e5394bda7cbd03fe
Xiaoxin Chen
67c66382e5394bda7cbd03ff
Qian Liu
claimed_verified
2025-03-27T09:05:18.358Z
612ee6a7b960e78c6d2319d4
https://cdn-avatars.huggingface.co/v1/production/uploads/612ee6a7b960e78c6d2319d4/2Hu9BaAyXbyh1vt0v1Qui.jpeg
Qian Liu
user
SivilTaram
67c66382e5394bda7cbd0400
Junxian He
Predictive Data Selection: The Data That Predicts Is the Data That
  Teaches
Language model pretraining involves training on extensive corpora, where data
quality plays a pivotal role. In this work, we aim to directly estimate the
contribution of data during pretraining and select pretraining data in an
efficient manner. Specifically, we draw inspiration from recent findings
showing that compression efficiency (i.e., the normalized loss) of diverse
models on certain text correlates strongly with their downstream performance,
when the text domain aligns with the downstream benchmark (Huang et al., 2024).
Building on this observation, we hypothesize that data on which model losses
are predictive of downstream abilities also contribute effectively to learning.
To leverage this insight, we introduce data selection based on data's
Predictive strength (Preselect), a lightweight and efficient data selection
method that requires training and deploying only a fastText-based scorer.
Through comprehensive experiments with 1B and 3B parameter models, we
demonstrate that models trained on 30B tokens selected with PreSelect surpasses
the performance of a vanilla baseline trained on 300B tokens, achieving a 10x
reduction in compute requirements. Furthermore, PreSelect significantly
outperforms other competitive data selection baselines, such as DCLM and
FineWeb-Edu on a scale of 3B models trained on 100B tokens. We open-source our
trained data selection scorer along with the curated datasets at
https://github.com/hkust-nlp/PreSelect.
A lightweight data selection method called PreSelect effectively reduces computational requirements while enhancing language model performance by focusing on data predictive of downstream abilities.
compression efficiency downstream performance data selection fastText predictive strength DCLM FineWeb-Edu