title: TinyStories: How Small Can Language Models Be and Still Speak Coherent
    English?
summary: Language models (LMs) are powerful tools for natural language processing, but
  they often struggle to produce coherent and fluent text when they are small.
  Models with around 125M parameters such as GPT-Neo (small) or GPT-2 (small) can
  rarely generate coherent and consistent English text beyond a few words even
  after extensive training. This raises the question of whether the emergence of
  the ability to produce coherent English text only occurs at larger scales (with
  hundreds of millions of parameters or more) and complex architectures (with
  many layers of global attention).
    In this work, we introduce TinyStories, a synthetic dataset of short stories
  that only contain words that a typical 3 to 4-year-olds usually understand,
  generated by GPT-3.5 and GPT-4. We show that TinyStories can be used to train
  and evaluate LMs that are much smaller than the state-of-the-art models (below
  10 million total parameters), or have much simpler architectures (with only one
  transformer block), yet still produce fluent and consistent stories with
  several paragraphs that are diverse and have almost perfect grammar, and
  demonstrate reasoning capabilities.
    We also introduce a new paradigm for the evaluation of language models: We
  suggest a framework which uses GPT-4 to grade the content generated by these
  models as if those were stories written by students and graded by a (human)
  teacher. This new paradigm overcomes the flaws of standard benchmarks which
  often requires the model's output to be very structures, and moreover provides
  a multidimensional score for the model, providing scores for different
  capabilities such as grammar, creativity and consistency.
    We hope that TinyStories can facilitate the development, analysis and
  research of LMs, especially for low-resource or specialized domains, and shed
  light on the emergence of language capabilities in LMs.
paper_authors._id: 6462dcfd77f63463fc60e948
paper_authors.name: Ronen Eldan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-17T08:05:04.429Z
paper_authors._id: 633723a80267ebcf0264c06b
paper_authors.avatarUrl: /avatars/22bb971597e9f3abfa343280a9d0f65f.svg
paper_authors.fullname: Ronen Eldan
paper_authors.type: user
paper_authors.user: roneneldan
paper_authors._id: 6462dcfd77f63463fc60e949
paper_authors.name: Yuanzhi Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-29T16:46:09.260Z
paper_authors._id: 641d5a313f04f9bf2da17e8c
paper_authors.avatarUrl: /avatars/4035f73c69ddb045573ad96db4f13f04.svg
paper_authors.fullname: Yuanzhi Li
paper_authors.type: user
paper_authors.user: Uushizhu1234
paper_title: TinyStories: How Small Can Language Models Be and Still Speak Coherent
    English?
paper_summary: Language models (LMs) are powerful tools for natural language processing, but
  they often struggle to produce coherent and fluent text when they are small.
  Models with around 125M parameters such as GPT-Neo (small) or GPT-2 (small) can
  rarely generate coherent and consistent English text beyond a few words even
  after extensive training. This raises the question of whether the emergence of
  the ability to produce coherent English text only occurs at larger scales (with
  hundreds of millions of parameters or more) and complex architectures (with
  many layers of global attention).
    In this work, we introduce TinyStories, a synthetic dataset of short stories
  that only contain words that a typical 3 to 4-year-olds usually understand,
  generated by GPT-3.5 and GPT-4. We show that TinyStories can be used to train
  and evaluate LMs that are much smaller than the state-of-the-art models (below
  10 million total parameters), or have much simpler architectures (with only one
  transformer block), yet still produce fluent and consistent stories with
  several paragraphs that are diverse and have almost perfect grammar, and
  demonstrate reasoning capabilities.
    We also introduce a new paradigm for the evaluation of language models: We
  suggest a framework which uses GPT-4 to grade the content generated by these
  models as if those were stories written by students and graded by a (human)
  teacher. This new paradigm overcomes the flaws of standard benchmarks which
  often requires the model's output to be very structures, and moreover provides
  a multidimensional score for the model, providing scores for different
  capabilities such as grammar, creativity and consistency.
    We hope that TinyStories can facilitate the development, analysis and
  research of LMs, especially for low-resource or specialized domains, and shed
  light on the emergence of language capabilities in LMs.
paper_ai_keywords: Language models (LMs) GPT-Neo (small) GPT-2 (small) TinyStories synthetic dataset short stories global attention transformer block fluent consistent diverse grammar reasoning capabilities GPT-4 evaluation framework multidimensional score