  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 relies on external resources, and
both require incorporating explicit documents into the context, which increases
execution costs and susceptibility to noise data. Recent works indicate that
LLMs have modeled rich knowledge, albeit not effectively triggered or awakened.
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, thereby awakening relevant knowledge in LLMs without relying on
external resources. Guided by IAG, we propose an imagine richer context method
for question answering (IMcQA). IMcQA consists of two modules: explicit
imagination, which generates a short dummy document by learning from long
context compression, and implicit imagination, which creates flexible adapters
by distilling from a teacher model with a long context. Experimental results on
three datasets demonstrate that IMcQA exhibits significant advantages in both
open-domain and closed-book settings, as well as in out-of-distribution
generalization. Our code will be available at
https://github.com/Xnhyacinth/IAG.
