Stabilizing Black-Box Prompt Optimization with Textual Regularization and Signal Aggregation
arXiv:2507.09839v2 Announce Type: replace Abstract: An increasing number of NLP applications interact with large language models (LLMs) through black-box APIs, making prompt engineering critical for controlling model behavior. Recent Automatic Prompt Optimization (APO) methods iteratively refine prompts using model-generated critiques (often called textual gradients), but they predominantly optimize from failures and underutilize information contained in correct predictions, leading to instability and semantic drift. We propose TRAS (Textual Regularization with Aggregated Signals), a feedback-
The proliferation of black-box LLM APIs necessitates more robust and efficient prompt optimization techniques to improve model control and performance.
This development addresses key limitations in current automatic prompt optimization, offering more stable and effective methods for interacting with large language models.
Prompt engineering for black-box LLMs becomes more reliable and less prone to instability and semantic drift, enhancing the usability and predictability of AI applications.
- · NLP application developers
- · Companies relying on LLM APIs
- · Users of AI-powered services
- · AI model developers
- · Inefficient manual prompt engineers
Improved performance and reliability of applications built on black-box LLMs.
Accelerated adoption of LLM-powered solutions across various industries due to increased stability and control.
Reduced barriers to entry for developing sophisticated AI applications, potentially leading to a more competitive and innovative AI ecosystem.
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