title: MiniPLM: Knowledge Distillation for Pre-Training Language Models
summary: Knowledge distillation (KD) is widely used to train small, high-performing
  student language models (LMs) using large teacher LMs. While effective in
  fine-tuning, KD during pre-training faces challenges in efficiency,
  flexibility, and effectiveness. Existing methods either incur high
  computational costs due to online teacher inference, require tokenization
  matching between teacher and student LMs, or risk losing the difficulty and
  diversity of the teacher-generated training data. To address these issues, we
  propose MiniPLM, a KD framework for pre-training LMs by refining the training
  data distribution with the teacher's knowledge. For efficiency, MiniPLM
  performs offline teacher LM inference, allowing KD for multiple student LMs
  without adding training-time costs. For flexibility, MiniPLM operates solely on
  the training corpus, enabling KD across model families. For effectiveness,
  MiniPLM leverages the differences between large and small LMs to enhance the
  difficulty and diversity of the training data, helping student LMs acquire
  versatile and sophisticated knowledge. Extensive experiments demonstrate that
  MiniPLM boosts the student LMs' performance on 9 widely used downstream tasks,
  improves the language modeling capabilities, and reduces pre-training
  computation. The benefit of MiniPLM extends to large pre-training scales,
  evidenced by the extrapolation of the scaling curves. Further analysis reveals
  that MiniPLM supports KD across model families and enhances the utilization of
  pre-training data. Our model, code, and data are available at
  https://github.com/thu-coai/MiniPLM.
paper_authors._id: 67186a1682e6fb032eb476ff
paper_authors.name: Yuxian Gu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-23T08:03:37.399Z
paper_authors._id: 624ac662102fcdff87be51b9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/624ac662102fcdff87be51b9/rzNahZFFkp194170tactJ.jpeg
paper_authors.fullname: Yuxian Gu
paper_authors.type: user
paper_authors.user: t1101675
paper_authors._id: 67186a1682e6fb032eb47700
paper_authors.name: Hao Zhou
paper_authors._id: 67186a1682e6fb032eb47701
paper_authors.name: Fandong Meng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-23T08:03:53.618Z
paper_authors._id: 64cb254871a7bbb60c17d5fa
paper_authors.avatarUrl: /avatars/5121fd5b7b55d275eba3947f3f4c034d.svg
paper_authors.fullname: Fandong Meng
paper_authors.type: user
paper_authors.user: fandong
paper_authors._id: 67186a1682e6fb032eb47702
paper_authors.name: Jie Zhou
paper_authors._id: 67186a1682e6fb032eb47703
paper_authors.name: Minlie Huang
paper_title: MiniPLM: Knowledge Distillation for Pre-Training Language Models
paper_summary: Knowledge distillation (KD) is widely used to train small, high-performing
  student language models (LMs) using large teacher LMs. While effective in
  fine-tuning, KD during pre-training faces challenges in efficiency,
  flexibility, and effectiveness. Existing methods either incur high
  computational costs due to online teacher inference, require tokenization
  matching between teacher and student LMs, or risk losing the difficulty and
  diversity of the teacher-generated training data. To address these issues, we
  propose MiniPLM, a KD framework for pre-training LMs by refining the training
  data distribution with the teacher's knowledge. For efficiency, MiniPLM
  performs offline teacher LM inference, allowing KD for multiple student LMs
  without adding training-time costs. For flexibility, MiniPLM operates solely on
  the training corpus, enabling KD across model families. For effectiveness,
  MiniPLM leverages the differences between large and small LMs to enhance the
  difficulty and diversity of the training data, helping student LMs acquire
  versatile and sophisticated knowledge. Extensive experiments demonstrate that
  MiniPLM boosts the student LMs' performance on 9 widely used downstream tasks,
  improves the language modeling capabilities, and reduces pre-training
  computation. The benefit of MiniPLM extends to large pre-training scales,
  evidenced by the extrapolation of the scaling curves. Further analysis reveals
  that MiniPLM supports KD across model families and enhances the utilization of
  pre-training data. Our model, code, and data are available at
  https://github.com/thu-coai/MiniPLM.
paper_ai_summary: MiniPLM is a knowledge distillation framework that enhances pre-training efficiency, flexibility, and effectiveness for student language models by refining the training data distribution using offline teacher inference.
paper_ai_keywords: knowledge distillation language models pre-training fine-tuning offline teacher inference tokenization matching training data distribution language modeling capabilities scaling curves