@inproceedings{gao-etal-2026-strunrag,
    title = "{S}tru{NRAG}: Evaluation of {OCR}-Induced Structural Noise on {RAG} Robustness",
    author = "Gao, Mengna  and
      Yin, Dapeng  and
      Zhu, Shuyue  and
      Hou, Bingxuan  and
      Ni, Zhanpeng  and
      Wang, Junli",
    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.955/",
    pages = "19129--19148",
    ISBN = "979-8-89176-395-1",
    abstract = "Retrieval-Augmented Generation (RAG) systems rely on Optical Character Recognition (OCR) to ingest knowledge from unstructured documents. However, OCR engines often struggle with complex layouts, introducing \textbf{Structural Noise}, such as line insertion and paragraph interleaving, which disrupts the semantic flow of the text. Existing evaluations largely overlook this dimension, operating on the assumption of structurally perfect input. To bridge this gap, we introduce StruNRAG, a dedicated benchmark for evaluating RAG robustness against OCR-induced structural perturbations. We construct a bilingual dataset of 2,132 question-answer pairs derived from complex Chinese and English documents and systematically inject three categories of real-world structural noise: line insertion, paragraph interleaving, and line interleaving. Our evaluation of mainstream retrievers and Large Language Models (LLMs) reveals a nuanced interaction between noise and pipeline stages: while structural distortions consistently degrade retrieval performance, the generation stage exhibits unexpected robustness. Advanced LLMs demonstrate robustness against local noise (e.g., line insertion), but struggle to maintain reasoning capabilities under severe structural disruption that fragments global context. These findings indicate that while LLMs are capable of compensating for minor parsing errors, future RAG optimizations must take into account the effects of structural noise. Our code and datasets are available at [https://github.com/GaoMengnana/StruNRAG](https://github.com/GaoMengnana/StruNRAG)."
}