  Retrieval Augmented Generation (RAG) has introduced a new paradigm for Large
Language Models (LLMs), aiding in the resolution of knowledge-intensive tasks.
However, current RAG models position LLMs as passive knowledge receptors,
thereby restricting their capacity for learning and comprehending external
knowledge. In this paper, we present ActiveRAG, an innovative RAG framework
that shifts from passive knowledge acquisition to an active learning mechanism.
This approach utilizes the Knowledge Construction mechanism to develop a deeper
understanding of external knowledge by associating it with previously acquired
or memorized knowledge. Subsequently, it designs the Cognitive Nexus mechanism
to incorporate the outcomes from both chains of thought and knowledge
construction, thereby calibrating the intrinsic cognition of LLMs. Our
experimental results demonstrate that ActiveRAG surpasses previous RAG models,
achieving a 5% improvement on question-answering datasets. All data and codes
are available at https://github.com/OpenMatch/ActiveRAG.
