title: UltraIF: Advancing Instruction Following from the Wild
summary: Instruction-following made modern large language models (LLMs) helpful
  assistants. However, the key to taming LLMs on complex instructions remains
  mysterious, for that there are huge gaps between models trained by open-source
  community and those trained by leading companies. To bridge the gap, we propose
  a simple and scalable approach UltraIF for building LLMs that can follow
  complex instructions with open-source data. UltraIF first decomposes real-world
  user prompts into simpler queries, constraints, and corresponding evaluation
  questions for the constraints. Then, we train an UltraComposer to compose
  constraint-associated prompts with evaluation questions. This prompt composer
  allows us to synthesize complicated instructions as well as filter responses
  with evaluation questions. In our experiment, for the first time, we
  successfully align LLaMA-3.1-8B-Base to catch up with its instruct version on 5
  instruction-following benchmarks without any benchmark information, using only
  8B model as response generator and evaluator. The aligned model also achieved
  competitive scores on other benchmarks. Moreover, we also show that UltraIF
  could further improve LLaMA-3.1-8B-Instruct through self-alignment, motivating
  broader use cases for the method. Our code will be available at
  https://github.com/kkk-an/UltraIF.
paper_authors._id: 67a57b1fdea89ffe80d9fe56
paper_authors.name: Kaikai An
paper_authors.status: claimed_verified
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paper_authors.fullname: KaikaiAn
paper_authors.type: user
paper_authors.user: kkk-an
paper_authors._id: 67a57b1fdea89ffe80d9fe57
paper_authors.name: Li Sheng
paper_authors.status: claimed_verified
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paper_authors.fullname: li sheng
paper_authors.type: user
paper_authors.user: bambisheng
paper_authors._id: 67a57b1fdea89ffe80d9fe58
paper_authors.name: Ganqu Cui
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/af6f5ee78f161d25ec0afc45d2def8eb.svg
paper_authors.fullname: Ganqu Cui
paper_authors.type: user
paper_authors.user: ganqu
paper_authors._id: 67a57b1fdea89ffe80d9fe59
paper_authors.name: Shuzheng Si
paper_authors.status: claimed_verified
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paper_authors._id: 637c99bbfe115289cfedfb44
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paper_authors.fullname: ssz
paper_authors.type: user
paper_authors.user: ssz1111
paper_authors._id: 67a57b1fdea89ffe80d9fe5a
paper_authors.name: Ning Ding
paper_authors._id: 67a57b1fdea89ffe80d9fe5b
paper_authors.name: Yu Cheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:53:56.027Z
paper_authors._id: 67017abfe4d49b157ac534d9
paper_authors.avatarUrl: /avatars/997e1b9f54b27a7728a9d4abfee4ba91.svg
paper_authors.fullname: Yu Cheng
paper_authors.type: user
paper_authors.user: ych133
paper_authors._id: 67a57b1fdea89ffe80d9fe5c
paper_authors.name: Baobao Chang
paper_title: UltraIF: Advancing Instruction Following from the Wild
paper_summary: Instruction-following made modern large language models (LLMs) helpful
  assistants. However, the key to taming LLMs on complex instructions remains
  mysterious, for that there are huge gaps between models trained by open-source
  community and those trained by leading companies. To bridge the gap, we propose
  a simple and scalable approach UltraIF for building LLMs that can follow
  complex instructions with open-source data. UltraIF first decomposes real-world
  user prompts into simpler queries, constraints, and corresponding evaluation
  questions for the constraints. Then, we train an UltraComposer to compose
  constraint-associated prompts with evaluation questions. This prompt composer
  allows us to synthesize complicated instructions as well as filter responses
  with evaluation questions. In our experiment, for the first time, we
  successfully align LLaMA-3.1-8B-Base to catch up with its instruct version on 5
  instruction-following benchmarks without any benchmark information, using only
  8B model as response generator and evaluator. The aligned model also achieved
  competitive scores on other benchmarks. Moreover, we also show that UltraIF
  could further improve LLaMA-3.1-8B-Instruct through self-alignment, motivating
  broader use cases for the method. Our code will be available at
  https://github.com/kkk-an/UltraIF.
paper_ai_summary: UltraIF enhances open-source LLMs' instruction-following capabilities by decomposing complex prompts and using evaluation questions to filter responses, achieving competitive scores on benchmarks.
paper_ai_keywords: UltraIF UltraComposer instruction-following prompts constraints evaluation questions LLaMA-3.1-8B-Base LLaMA-3.1-8B-Instruct self-alignment