@inproceedings{leung-etal-2026-classifying,
    title = "Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems",
    author = "Leung, Kin Kwan  and
      Belbahri, Mouloud  and
      Sui, Yi  and
      Labach, Alex  and
      Zhang, Xueying  and
      Rose, Stephen Anthony  and
      Cresswell, Jesse C.",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.147/",
    pages = "3185--3207",
    ISBN = "979-8-89176-380-7",
    abstract = "Retrieval-augmented generation (RAG) is a prevalent approach for building LLM-based question-answering systems that can take advantage of external knowledge databases. Due to the complexity of real-world RAG systems, there are many potential causes for erroneous outputs. Understanding the range of errors that can occur in practice is crucial for robust deployment. We present a new taxonomy of the error types that can occur in realistic RAG systems, examples of each, and practical advice for addressing them. Additionally, we curate a dataset of erroneous RAG responses annotated by error types. We then propose an auto-evaluation method aligned with our taxonomy that can be used in practice to track and address errors during development. Code and data are available at https://github.com/layer6ai-labs/rag-error-classification."
}