GPS: General Per-Sample Prompter

• URL: http://arxiv.org/abs/2511.21714v1
• Date: Tue, 18 Nov 2025 18:10:09 GMT
• Title: GPS: General Per-Sample Prompter
• Authors: Pawel Batorski, Paul Swoboda,
• Abstract summary: We propose GPS, the first general-purpose, per-sample prompting method.<n>GPS generates adaptive, input-specific prompts without extensive optimization and without access to a task-specific training set.
• Score: 13.775690509818753
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLMs are sensitive to prompting, with task performance often hinging on subtle, sometimes imperceptible variations in phrasing. As a result, crafting effective prompts manually remains challenging and time-consuming. Recent automatic prompting methods mitigate this difficulty but face three key limitations: (i) for each new task, they require large datasets to train good prompts;(ii) they rely on costly optimization loops that may take hours; (iii)they typically produce a single task-level prompt that does not adapt to the individual input problem to be solved. We propose GPS, the first general-purpose, per-sample prompting method. Without any task-specific tuning, GPS generates a tailored prompt for each unseen input, improving performance across diverse tasks. The prompter is trained with reinforcement learning on a suite of training tasks and includes a novel regularization for effectively adapting to per-sample prompting. Finally, we employ Minimum Bayes Risk decoding to stabilize inference. Empirically, GPS demonstrates competitive performance: we attain second best results among baselines on text simplification, third best results on summarization and on-par results on classification, while not training on any of these tasks, in contrast to the baselines. For in-domain prompting, we obtain sota on GSM8K. Our work shows the potential of a novel and effective paradigm for automatic prompting: generating adaptive, input-specific prompts without extensive optimization and without access to a task-specific training set. Our code is available at https://github.com/Batorskq/GPS.

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