Frederic Sadrieh

Prompt-Robust Language Models: Which Training Strategies Work?

The current robustness fine-tuning methods improve over standard fine-tuning and in-context learning, but the best-to-worst prompt gap remains as high as 40-57% of performance, and recent robustness-enhancing methods often fail to outperform the simplest data construction strategy: training on one template per batch.