MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks
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Computer Science > Computation and Language
Title:MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks
Abstract: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 this https URL.
| Comments: | Accepted to ACL 2026 (main conference). 28 pages. Code and data: this https URL |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.20729 [cs.CL] |
| (or arXiv:2605.20729v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20729
arXiv-issued DOI via DataCite (pending registration)
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