MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations

Sara Rosenthal; Yannis Katsis; Vraj Shah; Lihong He; Lucian Popa; Marina Danilevsky

MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations

Sara Rosenthal, Yannis Katsis, Vraj Shah, Lihong He, Lucian Popa, Marina Danilevsky

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Abstract

We present MTRAG-UN, a benchmark for exploring open challenges in multi-turn retrieval augment generation, a popular use of large language models. We release a benchmark of 666 tasks from 666 conversations containing over 2,800 conversation turns across 6 domains with accompanying corpora. Our experiments show that retrieval and generation models continue to struggle on conversations with UNanswerable, UNderspecified, and NONstandalone questions and UNclear responses. Our benchmark is available at https://github.com/IBM/mt-rag-benchmark

Anthology ID:: 2026.findings-acl.503
Volume:: Findings of the Association for Computational Linguistics: ACL 2026
Month:: July
Year:: 2026
Address:: San Diego, California, United States
Editors:: Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
Venue:: Findings
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 10363–10369
Language:
URL:: https://aclanthology.org/2026.findings-acl.503/
DOI:
Bibkey:
Cite (ACL):: Sara Rosenthal, Yannis Katsis, Vraj Shah, Lihong He, Lucian Popa, and Marina Danilevsky. 2026. MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations. In Findings of the Association for Computational Linguistics: ACL 2026, pages 10363–10369, San Diego, California, United States. Association for Computational Linguistics.
Cite (Informal):: MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations (Rosenthal et al., Findings 2026)
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PDF:: https://aclanthology.org/2026.findings-acl.503.pdf
Checklist:: 2026.findings-acl.503.checklist.pdf

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@inproceedings{rosenthal-etal-2026-mtrag,
    title = "{MTRAG}-{UN}: A Benchmark for Open Challenges in Multi-Turn {RAG} Conversations",
    author = "Rosenthal, Sara  and
      Katsis, Yannis  and
      Shah, Vraj  and
      He, Lihong  and
      Popa, Lucian  and
      Danilevsky, Marina",
    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://aclanthology.org/2026.findings-acl.503/",
    pages = "10363--10369",
    ISBN = "979-8-89176-395-1",
    abstract = "We present MTRAG-UN, a benchmark for exploring open challenges in multi-turn retrieval augment generation, a popular use of large language models. We release a benchmark of 666 tasks from 666 conversations containing over 2,800 conversation turns across 6 domains with accompanying corpora. Our experiments show that retrieval and generation models continue to struggle on conversations with UNanswerable, UNderspecified, and NONstandalone questions and UNclear responses. Our benchmark is available at https://github.com/IBM/mt-rag-benchmark"
}

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%0 Conference Proceedings
%T MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations
%A Rosenthal, Sara
%A Katsis, Yannis
%A Shah, Vraj
%A He, Lihong
%A Popa, Lucian
%A Danilevsky, Marina
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F rosenthal-etal-2026-mtrag
%X We present MTRAG-UN, a benchmark for exploring open challenges in multi-turn retrieval augment generation, a popular use of large language models. We release a benchmark of 666 tasks from 666 conversations containing over 2,800 conversation turns across 6 domains with accompanying corpora. Our experiments show that retrieval and generation models continue to struggle on conversations with UNanswerable, UNderspecified, and NONstandalone questions and UNclear responses. Our benchmark is available at https://github.com/IBM/mt-rag-benchmark
%U https://aclanthology.org/2026.findings-acl.503/
%P 10363-10369

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Markdown (Informal)

[MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations](https://aclanthology.org/2026.findings-acl.503/) (Rosenthal et al., Findings 2026)

MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations (Rosenthal et al., Findings 2026)

ACL

Sara Rosenthal, Yannis Katsis, Vraj Shah, Lihong He, Lucian Popa, and Marina Danilevsky. 2026. MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations. In Findings of the Association for Computational Linguistics: ACL 2026, pages 10363–10369, San Diego, California, United States. Association for Computational Linguistics.