title: PopAlign: Diversifying Contrasting Patterns for a More Comprehensive
    Alignment
summary: Alignment of large language models (LLMs) involves training models on
  preference-contrastive output pairs to adjust their responses according to
  human preferences. To obtain such contrastive pairs, traditional methods like
  RLHF and RLAIF rely on limited contrasting patterns, such as varying model
  variants or decoding temperatures. This singularity leads to two issues: (1)
  alignment is not comprehensive; and thereby (2) models are susceptible to
  jailbreaking attacks. To address these issues, we investigate how to construct
  more comprehensive and diversified contrasting patterns to enhance preference
  data (RQ1) and verify the impact of the diversification of contrasting patterns
  on model alignment (RQ2). For RQ1, we propose PopAlign, a framework that
  integrates diversified contrasting patterns across the prompt, model, and
  pipeline levels, introducing six contrasting strategies that do not require
  additional feedback labeling procedures. Regarding RQ2, we conduct thorough
  experiments demonstrating that PopAlign significantly outperforms existing
  methods, leading to more comprehensive alignment.
paper_authors._id: 6711cdb07da5c0a86f649718
paper_authors.name: Zekun Moore Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-18T12:37:40.404Z
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paper_authors.fullname: Zekun Moore Wang
paper_authors.type: user
paper_authors.user: ZenMoore
paper_authors._id: 6711cdb07da5c0a86f649719
paper_authors.name: Shawn Wang
paper_authors._id: 6711cdb07da5c0a86f64971a
paper_authors.name: Kang Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-18T08:55:13.210Z
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paper_authors.fullname: kang zhu
paper_authors.type: user
paper_authors.user: kangz
paper_authors._id: 6711cdb07da5c0a86f64971b
paper_authors.name: Jiaheng Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-18T12:38:22.673Z
paper_authors._id: 65377c30e48353201e6fdda0
paper_authors.avatarUrl: /avatars/a8f803b6f2e598eaee9c52c0d2ddfc16.svg
paper_authors.fullname: Jiaheng Liu
paper_authors.type: user
paper_authors.user: CheeryLJH
paper_authors._id: 6711cdb07da5c0a86f64971c
paper_authors.name: Ke Xu
paper_authors._id: 6711cdb07da5c0a86f64971d
paper_authors.name: Jie Fu
paper_authors._id: 6711cdb07da5c0a86f64971e
paper_authors.name: Wangchunshu Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-18T12:38:57.876Z
paper_authors._id: 628c8598ef14f971b698107f
paper_authors.avatarUrl: /avatars/3a4ad87e6b5f9e836a1160d869df1447.svg
paper_authors.fullname: Zhou
paper_authors.type: user
paper_authors.user: Wangchunshu
paper_authors._id: 6711cdb07da5c0a86f64971f
paper_authors.name: Wenhao Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-18T12:39:13.119Z
paper_authors._id: 641e5bf65f274a0a92c2f6a2
paper_authors.avatarUrl: /avatars/c15a54c51998c0e6367685e8e1737ec9.svg
paper_authors.fullname: Wenhao Huang
paper_authors.type: user
paper_authors.user: EZ-hwh
paper_title: PopAlign: Diversifying Contrasting Patterns for a More Comprehensive
    Alignment
paper_summary: Alignment of large language models (LLMs) involves training models on
  preference-contrastive output pairs to adjust their responses according to
  human preferences. To obtain such contrastive pairs, traditional methods like
  RLHF and RLAIF rely on limited contrasting patterns, such as varying model
  variants or decoding temperatures. This singularity leads to two issues: (1)
  alignment is not comprehensive; and thereby (2) models are susceptible to
  jailbreaking attacks. To address these issues, we investigate how to construct
  more comprehensive and diversified contrasting patterns to enhance preference
  data (RQ1) and verify the impact of the diversification of contrasting patterns
  on model alignment (RQ2). For RQ1, we propose PopAlign, a framework that
  integrates diversified contrasting patterns across the prompt, model, and
  pipeline levels, introducing six contrasting strategies that do not require
  additional feedback labeling procedures. Regarding RQ2, we conduct thorough
  experiments demonstrating that PopAlign significantly outperforms existing
  methods, leading to more comprehensive alignment.
paper_ai_summary: PopAlign enhances large language model alignment by introducing diverse contrasting patterns across prompts, models, and pipelines, leading to improved alignment compared to traditional methods.
paper_ai_keywords: large language models LLMs alignment preference-contrastive output pairs RLHF RLAIF contrasting patterns jailbreaking attacks PopAlign comprehensive alignment