A Multi-Dimensional Constraint Framework for Evaluating and Improving
  Instruction Following in Large Language Models
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.
6822e023b1df51252f95e958
Junjie Ye
claimed_verified
2025-05-14T07:35:57.239Z
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Junjie Ye
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Junjie-Ye
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Caishuang Huang
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Zhuohan Chen
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Wenjie Fu
admin_assigned
2025-05-14T10:10:19.727Z
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fuwenjie
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avonfwj
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Chenyuan Yang
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Leyi Yang
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Yilong Wu
admin_assigned
2025-05-14T10:11:11.451Z
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yilong
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wuyilong
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Peng Wang
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Meng Zhou
6822e023b1df51252f95e961
Xiaolong Yang
admin_assigned
2025-05-14T10:11:18.988Z
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Xiaolong  yang
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sean-xl-y
6822e023b1df51252f95e962
Tao Gui
6822e023b1df51252f95e963
Qi Zhang
6822e023b1df51252f95e964
Zhongchao Shi
6822e023b1df51252f95e965
Jianping Fan
6822e023b1df51252f95e966
Xuanjing Huang
admin_assigned
2025-05-14T10:11:25.379Z
67f9c4ee171948c38302ae0f
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Cqb3ijr_sZkpLhEEEEybK.png
Xuanjing Huang
user
xjhuang
A Multi-Dimensional Constraint Framework for Evaluating and Improving
  Instruction Following in Large Language Models
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.
An automated pipeline generates diverse, code-verifiable instruction-following test samples, revealing performance variation across constraints in large language models and enhancing instruction following via reinforcement learning.
instruction-following constraint framework constraint patterns constraint categories difficulty levels automated instruction generation constraint expansion conflict detection instruction rewriting reinforcement learning attention modules model parameters