How are Prompts Different in Terms of Sensitivity?

Sheng Lu; Hendrik Schuff; Iryna Gurevych

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arXiv · 2311.07230

How are Prompts Different in Terms of Sensitivity?

Sheng Lu·Hendrik Schuff·Iryna Gurevych

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Abstract

In-context learning (ICL) has become one of the most popular learning paradigms. While there is a growing body of literature focusing on prompt engineering, there is a lack of systematic analysis comparing the effects of prompts across different models and tasks. To address this gap, we present a comprehensive prompt analysis based on the sensitivity of a function. Our analysis reveals that sensitivity is an unsupervised proxy for model performance, as it exhibits a strong negative correlation with accuracy. We use gradient-based saliency scores to empirically demonstrate how different prompts affect the relevance of input tokens to the output, resulting in different levels of sensitivity. Furthermore, we introduce sensitivity-aware decoding which incorporates sensitivity estimation as a penalty term in the standard greedy decoding. We show that this approach is particularly helpful when information in the input is scarce. Our work provides a fresh perspective on the analysis of prompts, and contributes to a better understanding of the mechanism of ICL.

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Sheng Lu, Hendrik Schuff, Iryna Gurevych. 2024-06-20. How are Prompts Different in Terms of Sensitivity?. https://arxiv.org/abs/2311.07230

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