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Computer Science > Computation and Language

arXiv:2309.05463 (cs)
[Submitted on 11 Sep 2023]

Title:Textbooks Are All You Need II: phi-1.5 technical report

Authors:Yuanzhi Li, Sebastien Bubeck, Ronen Eldan, Allie Del Giorno,
Suriya Gunasekar, Yin Tat Lee
Download a PDF of the paper titled Textbooks Are All You Need II:
phi-1.5 technical report, by Yuanzhi Li and 5 other authors
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    Abstract: We continue the investigation into the power of smaller
    Transformer-based language models as initiated by \textbf
    {TinyStories} -- a 10 million parameter model that can produce
    coherent English -- and the follow-up work on \textbf{phi-1}, a
    1.3 billion parameter model with Python coding performance close
    to the state-of-the-art. The latter work proposed to use existing
    Large Language Models (LLMs) to generate ``textbook quality" data
    as a way to enhance the learning process compared to traditional
    web data. We follow the ``Textbooks Are All You Need" approach,
    focusing this time on common sense reasoning in natural language,
    and create a new 1.3 billion parameter model named \textbf
    {phi-1.5}, with performance on natural language tasks comparable
    to models 5x larger, and surpassing most non-frontier LLMs on
    more complex reasoning tasks such as grade-school mathematics and
    basic coding. More generally, \textbf{phi-1.5} exhibits many of
    the traits of much larger LLMs, both good -- such as the ability
    to ``think step by step" or perform some rudimentary in-context
    learning -- and bad, including hallucinations and the potential
    for toxic and biased generations -- encouragingly though, we are
    seeing improvement on that front thanks to the absence of web
    data. We open-source \textbf{phi-1.5} to promote further research
    on these urgent topics.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2309.05463 [cs.CL]
          (or arXiv:2309.05463v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2309.05463
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Suriya Gunasekar [view email]
[v1] Mon, 11 Sep 2023 14:01:45 UTC (59 KB)
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