TextReg: Mitigating Prompt Distributional Overfitting via Regularized Text-Space Optimization
arXiv:2605.21318v1 Announce Type: cross Abstract: Large language models (LLMs) are highly sensitive to the prompts used to specify task objectives and behavioral constraints. Many recent prompt optimization methods iteratively rewrite prompts using LLM-generated feedback, but the resulting prompts often become longer, accumulate narrow sample-specific rules, and generalize poorly beyond the training distribution. We study this failure mode as prompt distributional overfitting and argue that it reflects a lack of representation control in discrete text-space optimization. We formalize this view
As LLMs become ubiquitous, the limitations of current prompt optimization methods are surfacing, necessitating new approaches to improve their reliability and generalization.
This work addresses a critical vulnerability in LLM development—prompt overfitting—which impacts the robustness, scalability, and trustworthy deployment of AI systems.
The proposed 'TextReg' method offers a pathway to more resilient and less 'brittle' LLM applications by addressing a fundamental challenge in their interaction design.
- · AI developers
- · LLM application providers
- · Enterprises adopting AI
- · AI researchers
- · Companies relying on naive prompt engineering
- · LLM applications with poor generalization
Improved stability and predictability of LLM-powered systems will accelerate their adoption in critical applications.
Reduced prompt engineering overhead could make advanced LLM capabilities more accessible to a wider range of users and developers.
The development of more generalized prompts could lead to a 'meta-learning' improvement in how LLMs themselves learn from instructions, impacting future model architectures.
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Read at arXiv cs.LG