语言模型通过 elicited knowledge 的准备受益
Language Models Benefit from Preparation with Elicited Knowledge
AI总结：
针对依赖知识而非推理的问答任务，提出PREP提示技术，利用双语言模型生成相关信息并作答，在部件材料及常识推理数据集上平均准确率优于对比方法。
摘要：
零样本思维链（CoT）方法常用于语言模型（LMs）在需要多步推理的任务中进行问答（QA）。然而，一些QA任务更多地依赖于获取相关知识，而非串联推理步骤。我们提出了一种简单的提示技术，称为PREP，它涉及使用两个LM实例：第一个（LM1）生成相关信息，第二个（LM2）从用户处接收信息并回答问题。这种设计旨在更好地利用LM的指令遵循能力。PREP适用于各种QA任务，无需特定领域的提示工程。PREP是在一个包含100个QA问题的数据集上开发的，该数据集源自一个详尽的示意图数据集，指定了人工制品的部件和材料组成。这些问题询问两个人工制品中哪一个更不可能与另一个人工制品共享材料。此类问题探查了LM对不同人工制品部件结构中共享材料的知识。我们在我们的部件与材料数据集以及三个已发布的常识推理数据集上测试了我们的方法。在所有测试的数据集上，我们方法的平均准确率始终高于所有其他测试方法。
英文摘要：
The zero-shot chain of thought (CoT) approach is often used in question answering (QA) by language models (LMs) for tasks that require multiple reasoning steps. However, some QA tasks hinge more on accessing relevant knowledge than on chaining reasoning steps. We introduce a simple prompting technique, called PREP, that involves using two instances of LMs: the first (LM1) generates relevant information, and the second (LM2) receives the information from the user and answers the question. This design is intended to make better use of the LM's instruction-following capability. PREP is applicable across various QA tasks without domain-specific prompt engineering. PREP is developed on a dataset of 100 QA questions, derived from an extensive schematic dataset specifying artifact parts and material composition. These questions ask which of two artifacts is less likely to share materials with another artifact. Such questions probe the LM's knowledge of shared materials in the part structure of different artifacts. We test our method on our parts-and-materials dataset and three published commonsense reasoning datasets. The average accuracy of our method is consistently higher than that of all the other tested methods across all the tested datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
