Provable Adversarial Robustness in In-Context Learning
arXiv:2602.17743v1 Announce Type: new Abstract: Large language models adapt to new tasks through in-context learning (ICL) without parameter updates. Current theoretical explanations for this capability assume test tasks are drawn from a distribution similar to that seen during pretraining. This assumption overlooks adversarial distribution shifts that threaten real-world reliability. To address this gap, we introduce a distributionally robust meta-learning framework that provides worst-case performance guarantees for ICL under Wasserstein-based distribution shifts. Focusing on linear self-attention Transformers, we derive a non-asymptotic bound linking adversarial perturbation strength ($\rho$), model capacity ($m$), and the number of in-context examples ($N$). The analysis reveals that model robustness scales with the square root of its capacity ($\rho_{\text{max}} \propto \sqrt{m}$), while adversarial settings impose a sample complexity penalty proportional to the square of the per
arXiv:2602.17743v1 Announce Type: new Abstract: Large language models adapt to new tasks through in-context learning (ICL) without parameter updates. Current theoretical explanations for this capability assume test tasks are drawn from a distribution similar to that seen during pretraining. This assumption overlooks adversarial distribution shifts that threaten real-world reliability. To address this gap, we introduce a distributionally robust meta-learning framework that provides worst-case performance guarantees for ICL under Wasserstein-based distribution shifts. Focusing on linear self-attention Transformers, we derive a non-asymptotic bound linking adversarial perturbation strength ($\rho$), model capacity ($m$), and the number of in-context examples ($N$). The analysis reveals that model robustness scales with the square root of its capacity ($\rho_{\text{max}} \propto \sqrt{m}$), while adversarial settings impose a sample complexity penalty proportional to the square of the perturbation magnitude ($N_\rho - N_0 \propto \rho^2$). Experiments on synthetic tasks confirm these scaling laws. These findings advance the theoretical understanding of ICL's limits under adversarial conditions and suggest that model capacity serves as a fundamental resource for distributional robustness.
Executive Summary
This article proposes a distributionally robust meta-learning framework to provide worst-case performance guarantees for in-context learning (ICL) under adversarial distribution shifts. The framework is applied to linear self-attention Transformers, and the authors derive non-asymptotic bounds linking adversarial perturbation strength, model capacity, and the number of in-context examples. The analysis reveals that model robustness scales with the square root of its capacity, while adversarial settings impose a sample complexity penalty proportional to the square of the perturbation magnitude. Experiments on synthetic tasks confirm these scaling laws, advancing the theoretical understanding of ICL's limits under adversarial conditions.
Key Points
- ▸ The authors propose a distributionally robust meta-learning framework to address adversarial distribution shifts in ICL.
- ▸ The framework is applied to linear self-attention Transformers, and non-asymptotic bounds are derived for robustness and sample complexity.
- ▸ The analysis reveals that model robustness scales with the square root of its capacity and that adversarial settings impose a sample complexity penalty.
Merits
Advances theoretical understanding of ICL
The article provides a significant contribution to the theoretical understanding of in-context learning's limits under adversarial conditions, shedding light on the trade-offs between model capacity, sample complexity, and robustness.
Derives non-asymptotic bounds for robustness and sample complexity
The authors provide precise mathematical bounds that can be used to analyze and compare the robustness and sample complexity of different models and training procedures.
Demerits
Limited to linear self-attention Transformers
The framework and analysis are currently restricted to a specific class of models, which may limit their applicability and generalizability to other architectures and domains.
Experiments are performed on synthetic tasks
The authors' conclusions are drawn from experiments conducted on artificial tasks, which may not accurately reflect real-world scenarios and challenges.
Expert Commentary
This article is a significant contribution to the field of adversarial robustness in deep learning. The authors' framework and analysis provide a novel and rigorous approach to understanding the trade-offs between model capacity, sample complexity, and robustness. The article's findings have important implications for the design and training of robust models, and its results are a crucial step forward in the development of reliable and trustworthy AI systems. However, the article's limitations, such as its focus on linear self-attention Transformers and synthetic tasks, should be carefully considered and addressed in future work.
Recommendations
- ✓ Future work should aim to extend the framework and analysis to other architectures and domains.
- ✓ Experiments should be conducted on real-world tasks and datasets to validate the article's findings and conclusions.
