StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction
  Following
Authors: name: Jinnan Li, name: Jinzhe Li, name: Yue Wang, name: Yi Chang, name: Yuan Wu

Multi-turn instruction following capability constitutes a core competency of
large language models (LLMs) in real-world applications. Existing evaluation
benchmarks predominantly focus on fine-grained constraint satisfaction and
domain-specific capability assessment, yet overlook the crucial structural
dependency between dialogue turns that distinguishes multi-turn from
single-turn interactions. This structural dependency not only reflects user
intent but also establishes a second dimension for instruction following
evaluation beyond constraint satisfaction. To address this gap, we propose
StructFlowBench, a multi-turn instruction following benchmark with structural
flow modeling. The benchmark innovatively defines a structural flow framework
comprising six fundamental inter-turn relationships, which not only introduces
novel structural constraints for model evaluation but also serves as generation
parameters for creating customized dialogue flows tailored to specific
scenarios. Adopting established LLM-based automatic evaluation methodologies,
we conduct systematic evaluations of 13 leading open-source and closed-source
LLMs. Experimental results reveal significant deficiencies in current models'
comprehension of multi-turn dialogue structures. The code is available at
https://github.com/MLGroupJLU/StructFlowBench.