ActiveRAG: Revealing the Treasures of Knowledge via Active Learning
<|reference_start|>ActiveRAG: Revealing the Treasures of Knowledge via Active Learning: 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.<|reference_end|>
@article{xu2024activerag:,
    title={ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through
  Retrieval-Augmented Agents},
    author={Zhipeng Xu, Zhenghao Liu, Yukun Yan, Shuo Wang, Shi Yu, Zheni Zeng,
  Chaojun Xiao, Zhiyuan Liu, Ge Yu and Chenyan Xiong},
    journal={arXiv preprint arXiv:2402.13547},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13547},
    primaryClass={cs.CL}
}