The past decade has witnessed dramatic gains in natural language processing
and an unprecedented scaling of large language models. These developments have
been accelerated by the advent of few-shot techniques such as chain of thought
(CoT) prompting. Specifically, CoT pushes the performance of large language
models in a few-shot setup by augmenting the prompts with intermediate steps.
Despite impressive results across various tasks, the reasons behind their
success have not been explored. This work uses counterfactual prompting to
develop a deeper understanding of CoT-based few-shot prompting mechanisms in
large language models. We first systematically identify and define the key
components of a prompt: symbols, patterns, and text. Then, we devise and
conduct an exhaustive set of experiments across four different tasks, by
querying the model with counterfactual prompts where only one of these
components is altered. Our experiments across three models (PaLM, GPT-3, and
CODEX) reveal several surprising findings and brings into question the
conventional wisdom around few-shot prompting. First, the presence of factual
patterns in a prompt is practically immaterial to the success of CoT. Second,
our results conclude that the primary role of intermediate steps may not be to
facilitate learning how to solve a task. The intermediate steps are rather a
beacon for the model to realize what symbols to replicate in the output to form
a factual answer. Further, text imbues patterns with commonsense knowledge and
meaning. Our empirical and qualitative analysis reveals that a symbiotic
relationship between text and patterns explains the success of few-shot
prompting: text helps extract commonsense from the question to help patterns,
and patterns enforce task understanding and direct text generation.

本文研究了基于 chain of thought (CoT) prompting 的 few-shot 学习机制，使用反事实提示进行实验，并通过多个模型证实了 CoT 的成功不是由 pattern 的存在而来，而是其中一个目的是在输出中找到正确的单词以形成一个正确的答案。文本有助于从问题中提取通识知识和含义，模式则强化任务理解和直接文本生成。