arXiv:2505.17123v3 Announce Type: replace 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 g
The rapid advancement of LLMs necessitates more sophisticated evaluation methods beyond single-turn tasks, particularly as their interactive capabilities improve.
A comprehensive benchmark for multi-turn reasoning is crucial for accurately assessing LLM performance in interactive scenarios, guiding future development, and enabling more robust AI applications.
The introduction of MTR-Bench provides a standardized, scalable framework for evaluating LLMs on multi-turn reasoning, moving beyond foundational, single-turn assessments.
- · AI research institutions
- · LLM developers
- · AI-powered customer service platforms
- · Gaming AI companies
- · LLM providers with weak multi-turn reasoning
- · Developers relying solely on single-turn evaluation metrics
The MTR-Bench will accelerate the development of LLMs more capable of complex, interactive reasoning.
Improved multi-turn reasoning in LLMs will enable new applications requiring sustained dialogue and dynamic problem-solving.
These advanced interactive LLMs could form the basis for more sophisticated AI agents that can manage complex, multi-step workflows.
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Read at arXiv cs.CL