SemEval-2026 Task 8: MTRAGEval: Evaluating Multi-Turn RAG Conversations

Sara Rosenthal; Vraj Shah; Yannis Katsis; Marina Danilevsky

SemEval-2026 Task 8: MTRAGEval: Evaluating Multi-Turn RAG Conversations

Sara Rosenthal, Vraj Shah, Yannis Katsis, Marina Danilevsky

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Abstract

We present the results and findings from SemEval Task 8: MTRAGEval. MTRAGEval measures three Retrieval Augmented Generation (RAG) subtasks: A. Retrieval, B. Generate, and C. Retrieve+Generate (full RAG) on multi-turn conversations. The task is evaluated using MTRAG-UN, a new benchmark for Multi-Turn RAG focusing on Unanswerable, Underspecified, Non-Standalone, and Unclear Questions. The MTRAGEval task attracted strong participation with 107 registered teams and 92 submissions across all subtasks, and yielded several interesting findings on effective retrieval and query rewriting techniques, the use of ensemble models, and the compounding costs of retrieval errors on downstream generation quality.

Anthology ID:: 2026.semeval-1.447
Volume:: Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
Month:: July
Year:: 2026
Address:: San Diego, California, USA
Editors:: Ekaterina Kochmar, Debanjan Ghosh, Kai North, Mamoru Komachi
Venues:: SemEval | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 3673–3690
Language:
URL:: https://aclanthology.org/2026.semeval-1.447/
DOI:
Bibkey:
Cite (ACL):: Sara Rosenthal, Vraj Shah, Yannis Katsis, and Marina Danilevsky. 2026. SemEval-2026 Task 8: MTRAGEval: Evaluating Multi-Turn RAG Conversations. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3673–3690, San Diego, California, USA. Association for Computational Linguistics.
Cite (Informal):: SemEval-2026 Task 8: MTRAGEval: Evaluating Multi-Turn RAG Conversations (Rosenthal et al., SemEval 2026)
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PDF:: https://aclanthology.org/2026.semeval-1.447.pdf

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@inproceedings{rosenthal-etal-2026-semeval,
    title = "{S}em{E}val-2026 Task 8: {MTRAGE}val: Evaluating Multi-Turn {RAG} Conversations",
    author = "Rosenthal, Sara  and
      Shah, Vraj  and
      Katsis, Yannis  and
      Danilevsky, Marina",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.semeval-1.447/",
    pages = "3673--3690",
    ISBN = "979-8-89176-414-9",
    abstract = "We present the results and findings from SemEval Task 8: MTRAGEval. MTRAGEval measures three Retrieval Augmented Generation (RAG) subtasks: A. Retrieval, B. Generate, and C. Retrieve+Generate (full RAG) on multi-turn conversations. The task is evaluated using MTRAG-UN, a new benchmark for Multi-Turn RAG focusing on Unanswerable, Underspecified, Non-Standalone, and Unclear Questions. The MTRAGEval task attracted strong participation with 107 registered teams and 92 submissions across all subtasks, and yielded several interesting findings on effective retrieval and query rewriting techniques, the use of ensemble models, and the compounding costs of retrieval errors on downstream generation quality."
}

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%0 Conference Proceedings
%T SemEval-2026 Task 8: MTRAGEval: Evaluating Multi-Turn RAG Conversations
%A Rosenthal, Sara
%A Shah, Vraj
%A Katsis, Yannis
%A Danilevsky, Marina
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F rosenthal-etal-2026-semeval
%X We present the results and findings from SemEval Task 8: MTRAGEval. MTRAGEval measures three Retrieval Augmented Generation (RAG) subtasks: A. Retrieval, B. Generate, and C. Retrieve+Generate (full RAG) on multi-turn conversations. The task is evaluated using MTRAG-UN, a new benchmark for Multi-Turn RAG focusing on Unanswerable, Underspecified, Non-Standalone, and Unclear Questions. The MTRAGEval task attracted strong participation with 107 registered teams and 92 submissions across all subtasks, and yielded several interesting findings on effective retrieval and query rewriting techniques, the use of ensemble models, and the compounding costs of retrieval errors on downstream generation quality.
%U https://aclanthology.org/2026.semeval-1.447/
%P 3673-3690

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Markdown (Informal)

[SemEval-2026 Task 8: MTRAGEval: Evaluating Multi-Turn RAG Conversations](https://aclanthology.org/2026.semeval-1.447/) (Rosenthal et al., SemEval 2026)

SemEval-2026 Task 8: MTRAGEval: Evaluating Multi-Turn RAG Conversations (Rosenthal et al., SemEval 2026)

ACL

Sara Rosenthal, Vraj Shah, Yannis Katsis, and Marina Danilevsky. 2026. SemEval-2026 Task 8: MTRAGEval: Evaluating Multi-Turn RAG Conversations. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3673–3690, San Diego, California, USA. Association for Computational Linguistics.