A Multi-Dimensional Constraint Framework for Evaluating and Improving
  Instruction Following in Large Language Models
Authors: name: Junjie Ye, name: Caishuang Huang, name: Zhuohan Chen, name: Wenjie Fu, name: Chenyuan Yang, name: Leyi Yang, name: Yilong Wu, name: Peng Wang, name: Meng Zhou, name: Xiaolong Yang, name: Tao Gui, name: Qi Zhang, name: Zhongchao Shi, name: Jianping Fan, name: Xuanjing Huang

Instruction following evaluates large language models (LLMs) on their ability
to generate outputs that adhere to user-defined constraints. However, existing
benchmarks often rely on templated constraint prompts, which lack the diversity
of real-world usage and limit fine-grained performance assessment. To fill this
gap, we propose a multi-dimensional constraint framework encompassing three
constraint patterns, four constraint categories, and four difficulty levels.
Building on this framework, we develop an automated instruction generation
pipeline that performs constraint expansion, conflict detection, and
instruction rewriting, yielding 1,200 code-verifiable instruction-following
test samples. We evaluate 19 LLMs across seven model families and uncover
substantial variation in performance across constraint forms. For instance,
average performance drops from 77.67% at Level I to 32.96% at Level IV.
Furthermore, we demonstrate the utility of our approach by using it to generate
data for reinforcement learning, achieving substantial gains in instruction
following without degrading general performance. In-depth analysis indicates
that these gains stem primarily from modifications in the model's attention
modules parameters, which enhance constraint recognition and adherence. Code
and data are available in https://github.com/Junjie-Ye/MulDimIF.