Multi-hop Question Generation is the task of generating questions which
require the reader to reason over and combine information spread across
multiple passages using several reasoning steps. Chain-of-thought rationale
generation has been shown to improve performance on multi-step reasoning tasks
and make model predictions more interpretable. However, few-shot performance
gains from including rationales have been largely observed only in +100B
language models, and otherwise require large scale manual rationale annotation.
In this work, we introduce a new framework for applying chain-of-thought
inspired structured rationale generation to multi-hop question generation under
a very low supervision regime (8- to 128-shot). We propose to annotate a small
number of examples following our proposed multi-step rationale schema, treating
each reasoning step as a separate task to be performed by a generative language
model. We show that our framework leads to improved control over the difficulty
of the generated questions and better performance compared to baselines trained
without rationales, both on automatic evaluation metrics and in human
evaluation. Importantly, we show that this is achievable with a modest model
size.

提出一种新框架，将基于链式思考的结构化理性生成应用于低监督下的多跳问题生成中，其通过对少量例子进行注释并将每个推理步骤视为单独的任务来执行，显示了对生成问题难度的改进和比没有理性的基线模型更好的性能，而且模型规模较小。