title: A Multi-Dimensional Constraint Framework for Evaluating and Improving
    Instruction Following in Large Language Models
summary: 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.
paper_authors._id: 6822e023b1df51252f95e958
paper_authors.name: Junjie Ye
paper_authors.status: claimed_verified
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paper_authors.fullname: Junjie Ye
paper_authors.type: user
paper_authors.user: Junjie-Ye
paper_authors._id: 6822e023b1df51252f95e959
paper_authors.name: Caishuang Huang
paper_authors._id: 6822e023b1df51252f95e95a
paper_authors.name: Zhuohan Chen
paper_authors._id: 6822e023b1df51252f95e95b
paper_authors.name: Wenjie Fu
paper_authors.status: admin_assigned
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paper_authors.fullname: fuwenjie
paper_authors.type: user
paper_authors.user: avonfwj
paper_authors._id: 6822e023b1df51252f95e95c
paper_authors.name: Chenyuan Yang
paper_authors._id: 6822e023b1df51252f95e95d
paper_authors.name: Leyi Yang
paper_authors._id: 6822e023b1df51252f95e95e
paper_authors.name: Yilong Wu
paper_authors.status: admin_assigned
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paper_authors.fullname: yilong
paper_authors.type: user
paper_authors.user: wuyilong
paper_authors._id: 6822e023b1df51252f95e95f
paper_authors.name: Peng Wang
paper_authors._id: 6822e023b1df51252f95e960
paper_authors.name: Meng Zhou
paper_authors._id: 6822e023b1df51252f95e961
paper_authors.name: Xiaolong Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T10:11:18.988Z
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paper_authors.avatarUrl: /avatars/dc5802c5b76239737fa182a6cdfdae1b.svg
paper_authors.fullname: Xiaolong  yang
paper_authors.type: user
paper_authors.user: sean-xl-y
paper_authors._id: 6822e023b1df51252f95e962
paper_authors.name: Tao Gui
paper_authors._id: 6822e023b1df51252f95e963
paper_authors.name: Qi Zhang
paper_authors._id: 6822e023b1df51252f95e964
paper_authors.name: Zhongchao Shi
paper_authors._id: 6822e023b1df51252f95e965
paper_authors.name: Jianping Fan
paper_authors._id: 6822e023b1df51252f95e966
paper_authors.name: Xuanjing Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T10:11:25.379Z
paper_authors._id: 67f9c4ee171948c38302ae0f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Cqb3ijr_sZkpLhEEEEybK.png
paper_authors.fullname: Xuanjing Huang
paper_authors.type: user
paper_authors.user: xjhuang
paper_title: A Multi-Dimensional Constraint Framework for Evaluating and Improving
    Instruction Following in Large Language Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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