Imagination Augmented Generation: Learning to Imagine Richer Context for Question Answering over Large Language Models

arXiv:2403.15268v1 Announce Type: new
Abstract: 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 …

arxiv context cs.cl imagination imagine language language models large language large language models question question answering type