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

About

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
Commonsense ReasoningWinoGrande
Accuracy74
1442
Mathematical ReasoningGSM8K
Accuracy44.6
1398
Multi-task Language UnderstandingMMLU
Accuracy37.9
881
Mathematical ReasoningGSM8K (test)
Accuracy44.6
816
Physical Commonsense ReasoningPIQA
Accuracy77
696
Code GenerationHumanEval (test)
Pass@141.4
612
Question AnsweringOpenBookQA
Accuracy37.2
465
Commonsense ReasoningARC Challenge
Accuracy44.9
243
Common Sense ReasoningBoolQ
Accuracy75.8
240
ReasoningARC Easy
Accuracy76.1
233
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