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

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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.

Yuanzhi Li, S\'ebastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, Yin Tat Lee• 2023

Related benchmarks

TaskDatasetResultRank
Mathematical ReasoningGSM8K
Accuracy44.6
983
Multi-task Language UnderstandingMMLU
Accuracy37.9
842
Commonsense ReasoningWinoGrande
Accuracy74
776
Mathematical ReasoningGSM8K (test)
Accuracy44.6
751
Question AnsweringOpenBookQA
Accuracy37.2
465
Code GenerationHumanEval (test)
Pass@141.4
444
Physical Commonsense ReasoningPIQA
Accuracy77
329
ReasoningARC Easy
Accuracy76.1
183
Common Sense ReasoningHellaSwag
Accuracy48.4
164
Commonsense ReasoningARC Challenge
Accuracy44.9
132
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