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October 13, 2025Open Access

Same Question, Different Words: A Latent Adversarial Framework for Prompt Robustness

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Observational analysis reveals improved prompt robustness in language models, highlighting the efficacy of the latent adversarial framework.

Improvement of 0.5%-4% in the worst-case win-rate indicates notable performance gains.
Latent Adversarial Paraphrasing optimizes language models using learnable perturbations and Lagrangian regulation.
Extensive experiments validate LAP's effectiveness across multiple language model architectures.
The framework addresses prompt variations, reducing reliance on trial-and-error strategies for better deployment.

Fu et al. (2025) studied this question.

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Observational analysis reveals improved prompt robustness in language models, highlighting the efficacy of the latent adversarial framework.