System Message Generation for User Preferences using Open-Source Models
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.
67b42c5632929e97a92dee90
Minbyul Jeong
claimed_verified
2025-02-19T09:04:45.723Z
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Minbyul Jeong
user
Minbyul
67b42c5632929e97a92dee91
Jungho Cho
claimed_verified
2025-02-21T09:59:57.458Z
6596a87480a4560b8f9b9532
/avatars/33f39ee01c1648f1daca41a40a4964fb.svg
Christopher Cho
user
ChristopherCho
67b42c5632929e97a92dee92
Minsoo Khang
67b42c5632929e97a92dee93
Dawoon Jung
admin_assigned
2025-02-19T15:48:50.413Z
6268ec3bd4aa92e53a66d028
/avatars/af7fd3b4a49aca8b71617f3f17673227.svg
dawoon jung
user
noowad
67b42c5632929e97a92dee94
Teakgyu Hong
admin_assigned
2025-02-19T15:48:42.592Z
6551e62ca12178dd75f4592f
/avatars/f4213f9cb95847182bcca3281b1a042c.svg
Teakgyu Hong
user
tghong
System Message Generation for User Preferences using Open-Source Models
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.
SysGen generates system messages from datasets without them, improving alignment of assistant responses with user instructions in large language models.