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home / papers / 2026 / MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks

MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks

Paper · 2026 · Junhao Ruan, Abudukeyumu Abudula, Bei Li et al.

Summary

Conversational retrieval decides which evidence a RAG system can use, but its benchmarks have two kinds of flaws. Human annotators see only the document in front of them, so they miss other passages in the corpus that also answer a query. Heuristic synthesis such as CORAL turns Wikipedia structure into stilted questions. MTR-Suite addresses both problems with three parts:

  • MTR-Eval uses an ensemble of LLM judges to score existing datasets on four dimensions. It finds noisy and incomplete gold labels even in human-built resources like QReCC, QuAC, Doc2Dial and TopiOCQA.
  • MTR-Pipeline filters and chunks a corpus, then groups related passages via greedy traversal clustering. Cooperating LLM agents then write natural multi-turn dialogues whose queries point to unambiguous evidence, at about 1/400 of human annotation cost.
  • MTR-Bench is an openly released general-domain benchmark built from Wikipedia. It features abrupt topic switches, long-context ambiguity and wordy, production-like turns.

MTR-Bench separates strong and weak retrievers far more clearly than older datasets. This makes trustworthy retrieval evaluation cheaper and feasible for private corpora, such as financial ones.

Key points

  • MTR-Eval scores four dimensions: Query-Evidence Alignment, Evidence Completeness (tested by asking an LLM to pick the best document among the gold one and hard negatives), Answer-Evidence Faithfulness, and Answer Quality.
  • In 300 manually checked cases of evidence flagged as missing from the gold labels, 98% were truly valid answers (Fleiss' Kappa 0.92). Pearson correlation with human ratings was 0.82, 0.89 and 0.74 on three metrics.
  • MTR-Pipeline has three stages: knowledge base curation, clustering, and multi-agent generation. It was also applied to a financial-domain corpus, where retrievers were similarly weak.
  • Extended Recall@k on MTR-Bench shows the benchmark stays hard:
    • gte-Qwen2-7B-instruct reaches only 82.2% at R@1000, while bge-large-en-v1.5 plateaus at 47.0%.
    • Even the best retriever misses about 34% of relevant evidence in its top 100 results.
  • Code and data are released at github.com/rangehow/mtr-suite. The benchmark evaluates retrieval only, not end-to-end generation.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Tsinghua UniversityBeijing · China333 works · 87,019 citations🏢MeituanBeijing · China17 works · 3,769 citations🎓Northeastern UniversityShenyang · China42 works · 1,940 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 1,220 worksDialogue systems · 624 worksInformation retrieval & search · 473 works

Cite

Cite as Junhao Ruan, Abudukeyumu Abudula, Bei Li, Yongjing Yin, Xinyu Liu, Kechen Jiao, Xin Chen, Jingang Wang, Xunliang Cai, Tong Xiao and JingBo Zhu. 2026. MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1726

@inproceedings{ruan2026mtr,
  title = {{MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks}},
  author = {Junhao Ruan, Abudukeyumu Abudula, Bei Li, Yongjing Yin, Xinyu Liu, Kechen Jiao, Xin Chen, Jingang Wang, Xunliang Cai, Tong Xiao and JingBo Zhu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1726},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1726}
}

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