Chain-of-thought prompting (e.g., "Let's think step-by-step") primes large
language models to verbalize rationalization for their predictions. While
chain-of-thought can lead to dramatic performance gains, benefits appear to
emerge only for sufficiently large models (beyond 50B parameters). We show that
orders-of-magnitude smaller models (125M -- 1.3B parameters) can still benefit
from chain-of-thought prompting. To achieve this, we introduce Symbolic
Chain-of-Thought Distillation (SCoTD), a method to train a smaller student
model on rationalizations sampled from a significantly larger teacher model.
Experiments across several commonsense benchmarks show that: 1) SCoTD enhances
the performance of the student model in both supervised and few-shot settings,
and especially for challenge sets; 2) sampling many reasoning chains per
instance from the teacher is paramount; and 3) after distillation, student
chain-of-thoughts are judged by humans as comparable to the teacher, despite
orders of magnitude fewer parameters. We test several hypotheses regarding what
properties of chain-of-thought samples are important, e.g., diversity vs.
teacher likelihood vs. open-endedness. We release our corpus of
chain-of-thought samples and code.

本文介绍了一种名为 Symbolic Chain-of-Thought Distillation (SCoTD) 的方法，在巨大的语言模型的注释和参数的指导下，训练参数远低于 50B 的较小模型，在几种常识基准测试中证明了在有监督和少样本学习环境下可以提高性能。