论文标题
零射门开放域QA的自我提出的大语言模型
Self-Prompting Large Language Models for Zero-Shot Open-Domain QA
论文作者
论文摘要
开放域问答（ODQA）旨在回答问题，而无需明确提供特定的背景文档。在零拍设置中，该任务变得尤其具有挑战性，在零拍设置中，没有数据可用于培训定制的检索阅读器模型。虽然最近使用直接提示方法在零摄像机ODQA中表现出了最近的大型语言模型（LLM）在零弹药中的有效性，但这些方法仍然无法完全利用LLM的潜力。在本文中，我们提出了一个自我提出的框架，以明确利用LLMS参数中编码的大量知识及其强大的教学理解能力。具体而言，我们逐步提示LLMS以完全从头开始生成带有背景段落和解释的多个伪QA对。然后将这些生成的元素用于文化学习。实验结果表明，我们的方法显着超过了三个广泛使用的ODQA数据集的先前最先进的零摄像方法，甚至可以通过在完整的培训数据上使用各种自定义的微调模型来实现可比性的性能。我们的代码可在https://github.com/lockon-n/self-prompting上找到。
Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing specific background documents. This task becomes notably challenging in a zero-shot setting where no data is available to train tailored retrieval-reader models. While recent Large Language Models (LLMs) like GPT-3 have demonstrated their effectiveness in zero-shot ODQA using direct prompting methods, these methods still fall short of fully harnessing the potential of LLMs when implicitly invoked. In this paper, we propose a Self-Prompting framework to explicitly utilize the massive knowledge encoded in the parameters of LLMs and their strong instruction understanding abilities. Concretely, we prompt LLMs step by step to generate multiple pseudo QA pairs with background passages and explanations entirely from scratch. These generated elements are then utilized for in-context learning. Experimental results show that our method significantly surpasses previous state-of-the-art zero-shot methods on three widely-used ODQA datasets and even achieves comparable performance with various customized fine-tuned models on full training data. Our code is available at https://github.com/lockon-n/self-prompting.
