TinyStories: How Small Can Language Models Be and Still Speak Coherent
  English?
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.
6462dcfd77f63463fc60e948
Ronen Eldan
claimed_verified
2023-05-17T08:05:04.429Z
633723a80267ebcf0264c06b
/avatars/22bb971597e9f3abfa343280a9d0f65f.svg
Ronen Eldan
user
roneneldan
6462dcfd77f63463fc60e949
Yuanzhi Li
admin_assigned
2023-05-29T16:46:09.260Z
641d5a313f04f9bf2da17e8c
/avatars/4035f73c69ddb045573ad96db4f13f04.svg
Yuanzhi Li
user
Uushizhu1234
TinyStories: How Small Can Language Models Be and Still Speak Coherent
  English?
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.
TinyStories, a synthetic dataset of simplified stories, enables small or simple language models to produce fluent and coherent text with reasoning capabilities using an evaluation framework based on human-like grading.
TinyStories language models synthetic dataset short stories GPT-3.5 GPT-4 fluency consistency transformer block evaluation paradigm GPT-4 grading grammar creativity emerging language capabilities