The paper introduces Prompt-agnostic Language Models, focusing on train‑time techniques to embed prompt robustness during instruction tuning. It shows that mixing paraphrases in training causes significant gradient interference—57% of per‑paraphrasing gradients conflict—leading to the development of two gradient‑merging methods, Prompt‑TIES and Prompt‑DARE, which resolve this interference. Experiments across four models demonstrate that these methods substantially reduce per‑example variance and improve local robustness with minimal impact on overall accuracy.
By Frederic Sadrieh
The paper investigates how small lexical changes in prompts can cause large performance swings in large language models. Using a dataset of 132,000 prompt variants, the authors uncover a scaling law linking higher average task performance to lower variance and greater robustness. They identify domain-specific terminology and explicit action directives as key linguistic factors that stabilize prompts, and propose an automated Prompt-Refining Agent that reduces performance variance by 40.7% in code generation while maintaining or improving mean performance.
By Qipeng Xie, Zi Liang, Jiafei Wu, Yufei Chen, Weizheng Wang, Wenao Ma, Zhong Ming, Haiqin Yang, Kaishun Wu
As large language models (LLMs) grow more capable, they are increasingly deployed in context-rich settings where task inputs are often accompanied by long, partially irrelevant context. In a controlled setting, we find that state-of-the-art models often appear robust to task-irrelevant context at the aggregate level: prepending it to benchmark questions causes little change in overall accuracy.
Large language models can serve as capable long-horizon agents, but their out-of-distribution (OOD) generalization remains weak. We identify a key source of this failure as task insensitivity: when faced with similar but distinct tasks, models might apply patterns learned during training and fail to solve the task at hand.
arXiv:2609.15209v1 Announce Type: cross
Abstract: Automatic prompt optimization (APO) improves language-model programs by revising prompts from task feedback, yet it typically holds its training data...
By Tianyu Yuan, Zhuzhong Qian
arXiv:2606. 26918v1 Announce Type: new Abstract: Large language models can serve as capable long-horizon agents, but their out-of-distribution (OOD) generalization remains weak.
By Jingyu Liu, Xiaopeng Wu, Kehan Chen, Chuan Yu, Yong Liu