论文标题
推理电路：结构化理由的几乎没有射击的多台面问题产生
Reasoning Circuits: Few-shot Multihop Question Generation with Structured Rationales
论文作者
论文摘要
多跳问题生成是生成问题的任务，这些问题需要读者使用多个推理步骤进行推理并组合在多个段落中传播的信息。已经证明，经过深思熟虑的基本原理生成可以提高多步推理任务的性能，并使模型预测更加可解释。但是，仅在 +100b语言模型中观察到包括理由的射击性能收益很少，否则需要大规模的手动理由注释。在这项工作中，我们介绍了一个新的框架，用于在非常低的监督制度（8至128杆）下将受到思想链启发的结构性理性生成应用于多跳问题的生成。我们建议在我们提出的多步理由模式后注释少数示例，将每个推理步骤视为要由生成语言模型执行的单独任务。我们表明，与在自动评估指标和人类评估中，与未经理由训练的基线相比，与未经理由训练的基线相比，我们的框架可以改善对生成问题的难度和表现更好的控制。重要的是，我们证明这是可以使用适度的模型大小来实现的。
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.
