title: System Message Generation for User Preferences using Open-Source Models
summary: System messages play a crucial role in interactions with large language
  models (LLMs), often serving as prompts to initiate conversations. Through
  system messages, users can assign specific roles, perform intended tasks,
  incorporate background information, specify various output formats and
  communication styles. Despite such versatility, publicly available data are
  often lack system messages and subject to strict license constraints in the
  industry field. Manual labeling of publicly available data with system messages
  that align with user instructions demands significant resources. In view of
  such challenges, our work introduces SysGen, a pipeline for generating system
  messages with better aligned assistant responses from the supervised
  fine-tuning dataset without system messages. Training on SysGen data has
  demonstrated substantial improvements in the alignment of model responses with
  system messages and user instructions, as demonstrated across various
  open-source models on the Multifacet benchmark, while maintaining minimal
  impact on other unseen benchmarks such as Open LLM Leaderboard 2. Our
  qualitative analysis highlights the importance of diverse system messages to
  ensure better adaptability across different contexts.
paper_authors._id: 67b42c5632929e97a92dee90
paper_authors.name: Minbyul Jeong
paper_authors.status: claimed_verified
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paper_authors.fullname: Minbyul Jeong
paper_authors.type: user
paper_authors.user: Minbyul
paper_authors._id: 67b42c5632929e97a92dee91
paper_authors.name: Jungho Cho
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/33f39ee01c1648f1daca41a40a4964fb.svg
paper_authors.fullname: Christopher Cho
paper_authors.type: user
paper_authors.user: ChristopherCho
paper_authors._id: 67b42c5632929e97a92dee92
paper_authors.name: Minsoo Khang
paper_authors._id: 67b42c5632929e97a92dee93
paper_authors.name: Dawoon Jung
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-19T15:48:50.413Z
paper_authors._id: 6268ec3bd4aa92e53a66d028
paper_authors.avatarUrl: /avatars/af7fd3b4a49aca8b71617f3f17673227.svg
paper_authors.fullname: dawoon jung
paper_authors.type: user
paper_authors.user: noowad
paper_authors._id: 67b42c5632929e97a92dee94
paper_authors.name: Teakgyu Hong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-19T15:48:42.592Z
paper_authors._id: 6551e62ca12178dd75f4592f
paper_authors.avatarUrl: /avatars/f4213f9cb95847182bcca3281b1a042c.svg
paper_authors.fullname: Teakgyu Hong
paper_authors.type: user
paper_authors.user: tghong
paper_title: System Message Generation for User Preferences using Open-Source Models
paper_summary: System messages play a crucial role in interactions with large language
  models (LLMs), often serving as prompts to initiate conversations. Through
  system messages, users can assign specific roles, perform intended tasks,
  incorporate background information, specify various output formats and
  communication styles. Despite such versatility, publicly available data are
  often lack system messages and subject to strict license constraints in the
  industry field. Manual labeling of publicly available data with system messages
  that align with user instructions demands significant resources. In view of
  such challenges, our work introduces SysGen, a pipeline for generating system
  messages with better aligned assistant responses from the supervised
  fine-tuning dataset without system messages. Training on SysGen data has
  demonstrated substantial improvements in the alignment of model responses with
  system messages and user instructions, as demonstrated across various
  open-source models on the Multifacet benchmark, while maintaining minimal
  impact on other unseen benchmarks such as Open LLM Leaderboard 2. Our
  qualitative analysis highlights the importance of diverse system messages to
  ensure better adaptability across different contexts.
paper_ai_keywords: large language models LLMs system messages prompts supervised fine-tuning alignment Multifacet benchmark Open LLM Leaderboard 2 qualitative analysis