System Prompt Optimization with Meta-Learning
Large Language Models (LLMs) have shown remarkable capabilities, with
optimizing their input prompts playing a pivotal role in maximizing their
performance. However, while LLM prompts consist of both the task-agnostic
system prompts and task-specific user prompts, existing work on prompt
optimization has focused on user prompts specific to individual queries or
tasks, and largely overlooked the system prompt that is, once optimized,
applicable across different tasks and domains. Motivated by this, we introduce
the novel problem of bilevel system prompt optimization, whose objective is to
design system prompts that are robust to diverse user prompts and transferable
to unseen tasks. To tackle this problem, we then propose a meta-learning
framework, which meta-learns the system prompt by optimizing it over various
user prompts across multiple datasets, while simultaneously updating the user
prompts in an iterative manner to ensure synergy between them. We conduct
experiments on 14 unseen datasets spanning 5 different domains, on which we
show that our approach produces system prompts that generalize effectively to
diverse user prompts. Also, our findings reveal that the optimized system
prompt enables rapid adaptation even to unseen tasks, requiring fewer
optimization steps for test-time user prompts while achieving improved
performance.
68269a1eaa8aded616d280a0
Yumin Choi
claimed_verified
2025-05-16T07:12:06.309Z
64cfa0b9749587dbe01d0079
/avatars/93ca0a1d9c5578d052c5af0d4d1a0252.svg
Yumin Choi
user
YuminChoi
68269a1eaa8aded616d280a1
Jinheon Baek
admin_assigned
2025-05-16T08:10:10.549Z
63036b6c5c70c21d0ea79d48
/avatars/a7eb03f5cbd4eaa09fe807bbed8bc0f7.svg
Jinheon Baek
user
jinheon
68269a1eaa8aded616d280a2
Sung Ju Hwang
System Prompt Optimization with Meta-Learning
Large Language Models (LLMs) have shown remarkable capabilities, with
optimizing their input prompts playing a pivotal role in maximizing their
performance. However, while LLM prompts consist of both the task-agnostic
system prompts and task-specific user prompts, existing work on prompt
optimization has focused on user prompts specific to individual queries or
tasks, and largely overlooked the system prompt that is, once optimized,
applicable across different tasks and domains. Motivated by this, we introduce
the novel problem of bilevel system prompt optimization, whose objective is to
design system prompts that are robust to diverse user prompts and transferable
to unseen tasks. To tackle this problem, we then propose a meta-learning
framework, which meta-learns the system prompt by optimizing it over various
user prompts across multiple datasets, while simultaneously updating the user
prompts in an iterative manner to ensure synergy between them. We conduct
experiments on 14 unseen datasets spanning 5 different domains, on which we
show that our approach produces system prompts that generalize effectively to
diverse user prompts. Also, our findings reveal that the optimized system
prompt enables rapid adaptation even to unseen tasks, requiring fewer
optimization steps for test-time user prompts while achieving improved
performance.
A meta-learning framework for optimizing system prompts in Large Language Models (LLMs) improves generalization across diverse tasks and datasets.
Large Language Models (LLMs) bilevel system prompt optimization meta-learning system prompts user prompts unseen datasets unseen tasks rapid adaptation