@inproceedings{yang-etal-2026-knowledge-infused,
    title = "Knowledge-Infused Multi-Bit Watermarking for {RAG} Knowledge Bases",
    author = "Yang, Ke  and
      Yuan, Shuguang  and
      Yu, Jing  and
      Chen, Chi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1066/",
    pages = "21195--21218",
    ISBN = "979-8-89176-395-1",
    abstract = "Retrieval-Augmented Generation (RAG) enhances the factual accuracy of Large Language Model (LLM) outputs based on external knowledge bases. These knowledge bases often carry significant intellectual property (IP) value, raising the urgent need for robust watermarking techniques to protect IP. However, existing RAG watermarking methods remain in their infancy, facing challenges such as limited encoding capacity and potential degradation of RAG performance or knowledge quality. In this paper, we propose knowledge-infused and multi-bit watermarking (KMW) for RAG knowledge bases. It generates watermark text to infuse the knowledge base by benign knowledge completion and a tailored generative watermarking algorithm. Each generated text can carry a multi-bit watermark segment. For effective detection, we design a Watermark Text Indexer that optimizes queries for steady retrieval of watermarked texts. Experiments on multiple datasets and LLMs show KMW reliably extracts watermarks from adversarial RAGs. It is robust against knowledge selection, alteration, expansion, and RAG setting restrictions, while remaining stealthy and secure. This highlights that KMW ensures effective IP protection for RAG systems. Our code is available here https://github.com/iieSKLCSDsg/KMW."
}