MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation

• URL: http://arxiv.org/abs/2505.17123v2
• Date: Mon, 26 May 2025 03:49:45 GMT
• Title: MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation
• Authors: Xiaoyuan Li, Keqin Bao, Yubo Ma, Moxin Li, Wenjie Wang, Rui Men, Yichang Zhang, Fuli Feng, Dayiheng Liu, Junyang Lin,
• Abstract summary: We present MTR-Bench for Large Language Models' Multi-Turn Reasoning evaluation.<n>Comprising 4 classes, 40 tasks, and 3600 instances, MTR-Bench covers diverse reasoning capabilities.<n>MTR-Bench features fully-automated framework spanning both dataset constructions and model evaluations.
• Score: 56.87891213797931
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks. However, current evaluations predominantly focus on single-turn reasoning scenarios, leaving interactive tasks largely unexplored. We attribute it to the absence of comprehensive datasets and scalable automatic evaluation protocols. To fill these gaps, we present MTR-Bench for LLMs' Multi-Turn Reasoning evaluation. Comprising 4 classes, 40 tasks, and 3600 instances, MTR-Bench covers diverse reasoning capabilities, fine-grained difficulty granularity, and necessitates multi-turn interactions with the environments. Moreover, MTR-Bench features fully-automated framework spanning both dataset constructions and model evaluations, which enables scalable assessment without human interventions. Extensive experiments reveal that even the cutting-edge reasoning models fall short of multi-turn, interactive reasoning tasks. And the further analysis upon these results brings valuable insights for future research in interactive AI systems.

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