用于零样本开放域问答的自提示大语言模型
Self-Prompting Large Language Models for Zero-Shot Open-Domain QA
AI总结：
本文提出自提示框架，通过让大语言模型从零生成伪问答对及背景解释用于上下文学习，在零样本开放域问答上显著超越现有方法并接近全数据微调模型性能。
摘要：
开放域问答（ODQA）旨在不显式提供特定背景文档的情况下回答问题。在零样本设定下，由于没有可用数据来训练定制的检索-阅读器模型，该任务变得尤为具有挑战性。尽管近期像GPT-3这样的大语言模型（LLMs）通过直接提示方法在零样本ODQA中展现了有效性，但这些方法在隐式调用时仍未能充分发挥LLMs的潜力。本文提出了一种自提示（Self-Prompting）框架，以显式利用LLMs参数中编码的海量知识及其强大的指令理解能力。具体而言，我们逐步提示LLMs完全从零开始生成多个带有背景段落和解释的伪问答对，然后将这些生成元素用于上下文学习。实验结果表明，我们的方法在三个广泛使用的ODQA数据集上显著超越了先前最先进的零样本方法，甚至取得了与在完整训练数据上定制的各种微调模型相当的性能。代码可在https://github.com/lockon-n/self-prompting获取。
英文摘要：
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.
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。
