ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents

Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to leverage external knowledge, enhancing their performance on knowledge-intensive tasks. However, existing RAG models often treat LLMs as passive recipients of information, which can lead to interference from noisy retrieved content. In this paper, we introduce ActiveRAG, a multi-agent framework that mimics human learning behavior to help LLMs actively engage with and learn from retrieved evidence. ActiveRAG designs a knowledge assimilation agent to form the knowledge understanding by associating external knowledge with the parametric memory of LLMs. Then our model employs the thought accommodation agent to calibrate the internal thought of LLMs for response refinement. Our experiments show that ActiveRAG achieves a 10\% improvement over vanilla RAG on various question-answering benchmarks. Further analysis reveals that ActiveRAG mitigates the impact of noisy retrievals, alleviates conflicts between external knowledge and parametric memory and improves the self-consistency of LLMs in answering the question. All data and codes are available at this https URL.