Retrieval-Augmented-Generation and Gener-ation-Augmented-Generation have been
proposed to enhance the knowledge required for question answering over Large
Language Models (LLMs). However, the former depends on external resources, and
both require incorporating the explicit documents into the context, which
results in longer contexts that lead to more resource consumption. Recent works
indicate that LLMs have modeled rich knowledge, albeit not effectively
triggered or activated. Inspired by this, we propose a novel
knowledge-augmented framework, Imagination-Augmented-Generation (IAG), which
simulates the human capacity to compensate for knowledge deficits while
answering questions solely through imagination, without relying on external
resources. Guided by IAG, we propose an imagine richer context method for
question answering (IMcQA), which obtains richer context through the following
two modules: explicit imagination by generating a short dummy document with
long context compress and implicit imagination with HyperNetwork for generating
adapter weights. Experimental results on three datasets demonstrate that IMcQA
exhibits significant advantages in both open-domain and closed-book settings,
as well as in both in-distribution performance and out-of-distribution
generalizations. Our code will be available at
this https URL

提出了一种基于想象力的知识增强框架（IAG）和适用于问题回答的想象力丰富上下文方法（IMcQA），通过模拟人类补偿知识缺陷的能力，实现了在回答问题时仅依靠想象而不依赖外部资源。在三个数据集上的实验结果表明，IMcQA 在开放领域和封闭书籍设置中，以及分布内性能和分布外推广方面都具有显著优势。