@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems
%A Leung, Kin Kwan
%A Belbahri, Mouloud
%A Sui, Yi
%A Labach, Alex
%A Zhang, Xueying
%A Rose, Stephen Anthony
%A Cresswell, Jesse C.
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F leung-etal-2026-classifying
%X 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.
%U https://aclanthology.org/2026.eacl-long.147/
%P 3185-3207
Markdown (Informal)
[Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems](https://aclanthology.org/2026.eacl-long.147/) (Leung et al., EACL 2026)
ACL