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With growing capabilities of large language models, prompting them has becomethe dominant way to access them. This has motivated the development ofstrategies for automatically selecting effective language prompts. In thispaper, we introduce prompt flatness, a new metric to quantify the expectedutility of a language prompt. This metric is inspired by flatnessregularization in statistical learning that quantifies the robustness of themodel towards its parameter perturbations. We provide theoretical foundationsfor this metric and its relationship with other prompt selection metrics,providing a comprehensive understanding of existing methods. Empirically, weshow that combining prompt flatness with existing metrics improves bothperformance and sample efficiency. Our metric outperforms the previous promptselection metrics with an average increase of 5% in accuracy and 10% in Pearsoncorrelation across 6 classification benchmarks.

Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency