mlsanityData to 7 Oct 2026

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

arXiv 2605.20729 ↗First posted 20 May 2026cs.CLcited by 0

On the map: Retrieval-augmented generation → Conversational and speech RAG

Abstract (arXiv)

Accurate evaluation of conversational retrieval is pivotal for advancing Retrieval-Augmented Generation (RAG) systems. However, existing conversational retrieval benchmarks suffer from costly, sparse human annotation or rigid, unnatural automated heuristics. To address these challenges, we introduce MTR-Suite, a unified framework for auditing, synthesizing, and benchmarking retrieval. It features: (1) MTR-Eval, an LLM-based auditor quantifying alignment gaps in previous benchmarks; (2) MTR-Pipeline, a multi-agent system using greedy traversal clustering to generate high-fidelity dialogues at 1/400th human cost; and (3) MTR-Bench, a rigorous general-domain benchmark. MTR-Bench mimics production-style challenges (hard topic switching, verbosity), offering superior discriminative power. We make our code and data publicly available to facilitate future research at https://github.com/rangehow/mtr-suite.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Conversational and speech RAG, one of 15 directions in Retrieval-augmented generation. Builds on 28 of its 41 references that are in the map. First posted on arXiv 20 May 2026.

Builds on

28 of 41 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation30 Oct

Other references · 27

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  5. 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,151
  6. 2024The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scalecited by 1,104
  7. 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,096
  8. 2024SimPO: Simple Preference Optimization with a Reference-Free Rewardcited by 951
  9. 2018CoQA: A Conversational Question Answering Challengecited by 674
  10. 2024Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inferencecited by 527
  11. 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 466
  12. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 452
15 more
  1. 2018QuAC : Question Answering in Contextcited by 379
  2. 2024mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrievalcited by 233
  3. 2024Generative Representational Instruction Tuningcited by 227
  4. 2020Open-Domain Question Answering Goes Conversational via Question Rewritingcited by 147
  5. 2023How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrievalcited by 116
  6. 2024Jasper and Stella: distillation of SOTA embedding modelscited by 109
  7. 2021TopiOCQA: Open-domain Conversational Question Answering with Topic Switchingcited by 95
  8. 2024ChatQA: Surpassing GPT-4 on Conversational QA and RAGcited by 90
  9. 2020doc2dial: A Goal-Oriented Document-Grounded Dialogue Datasetcited by 88
  10. 2024Making Text Embedders Few-Shot Learnerscited by 78
  11. 2020TREC CAsT 2019: The Conversational Assistance Track Overviewcited by 64
  12. 2024LongEmbed: Extending Embedding Models for Long Context Retrievalcited by 47
  13. 2023MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretrainingcited by 33
  14. 2022INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactionscited by 27
  15. 2024Multi-Document Grounded Multi-Turn Synthetic Dialog Generationcited by 7

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  1. 2025MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems
  2. 2026MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations
  3. 2025Convomem Benchmark: Why Your First 150 Conversations Don't Need RAG
  4. 2026Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA
  5. 2024RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
  6. 2024Toward Conversational Agents with Context and Time Sensitive Long-term Memory
  7. 2025Conversational Gold: Evaluating Personalized Conversational Search System using Gold Nuggets
  8. 2026Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents