How are Prompts Different in Terms of Sensitivity? (2311.07230v2)

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.

• The paper demonstrates that lower sensitivity in prompt engineering correlates with higher accuracy, supported by strong statistical evidence (Pearson = -0.8764, p ≪ 0.01).
• The paper employs gradient-based saliency scores to evaluate prompt token influence on outputs, suggesting refined prompt design can enhance model stability.
• The paper introduces sensitivity-aware decoding as a practical strategy to boost performance in scenarios with limited input information.

Analyzing Sensitivity in Prompt Engineering for In-Context Learning

The paper "How are Prompts Different in Terms of Sensitivity?" presents a detailed analysis of prompt sensitivity in the context of in-context learning (ICL) with LLMs. The authors propose sensitivity as an unsupervised proxy for model performance, demonstrating a negative correlation between sensitivity and accuracy across different models and tasks. This investigation provides significant insights into prompt engineering and the mechanics of ICL, offering improvements and understanding of model behavior in response to prompts.

Examination of Sensitivity as a Complexity Measure

Sensitivity, as explored in this paper, is an intriguing approach that leverages the notion from Boolean function sensitivity, focusing on how variations in input affect output. The researchers utilize gradient-based saliency scores to elucidate how prompts influence the relevance of input tokens to the output, revealing that variations in sensitivity can be indicative of model performance deviations. The finding that lower sensitivity correlates strongly with higher accuracy (Pearson correlation coefficient of -0.8764, p-value ≪ 0.01) underscores sensitivity's utility as a performance measure.

Exploration of Prompts and Model Response

A wide array of natural language tasks, including sentiment analysis, natural language inference, and common-sense reasoning, were utilized to evaluate the sensitivity of different prompts. The paper examined human-designed prompts alongside those generated by LLMs, exploring various styles from context-faithful prompting to chain-of-thought and instruction-based prompts. Notably, sensitivity-aware decoding was introduced as a strategy that incorporates sensitivity estimation into the decoding process to enhance performance, particularly in scenarios with limited input information.

Saliency and Sensitivity Implications

An intriguing aspect of the research is the focus on gradient-based saliency scores, which highlight the greater influence of prompt tokens compared to input tokens on model outputs. This observation supports the hypothesis that prompts can be engineered to reduce sensitivity, thereby enhancing model stability and performance. Moreover, it suggests a potential role of memory in ICL, where pre-trained knowledge aids in model prediction, often overshadowing input specifics.

Model Behavior and Decoding Strategies

Through analysis, it was discovered that specific models, such as the Flan-T5, exhibit difficulty when presented with zero-prompts due to a lack of numeric index adherence, hinting at the importance of instruction clarity. Additionally, models displayed varying levels of sensitivity response influenced by different decoding strategies like greedy decoding versus Top-k sampling. The latter was found to amplify sensitivity effects.

Practical and Theoretical Implications

The implications of this research extend into both practical prompt engineering for LLM deployment and theoretical understanding of ICL mechanisms. Practically, leveraging sensitivity as a performance measure could simplify model evaluation across numerous applications where labeled data for accuracy testing is scarce. Theoretically, the paper sheds light on the role of implicit gradient descent in ICL and points towards enhanced methods for prompt construction that could yield more robust, less sensitive model outputs.

The consideration of sensitivity as a central feature in prompt engineering heralds promising directions for developing more nuanced models, where future advancements might focus on refining sensitivity-aware methods and further dissecting the saliency dynamics within LLMs. This research thus contributes a noteworthy perspective to the burgeoning field of prompt engineering and ICL, with potential future impacts on more reliable AI implementations.