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October 8, 2025Open Access

Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions

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Survey evaluates robustness in large language models, highlighting adversarial and ood challenges, suggesting future research directions.

The survey identifies key factors affecting the robustness of large language models, emphasizing the need for stability in various scenarios.
Adversarial robustness is explored, focusing on issues like noise prompts and data attacks, which can impact model performance significantly.
The evaluation of robustness is thoroughly discussed, including new datasets, metrics, and tools to verify model reliability in different conditions.
Future research opportunities are highlighted, pointing to the need for innovative strategies to improve robustness in unexpected application settings.

Zhang et al. (2025) studied this question.

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