ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs (2410.12405v1)

Abstract: LLMs have demonstrated impressive capabilities across various tasks, but their performance is highly sensitive to the prompts utilized. This variability poses challenges for accurate assessment and user satisfaction. Current research frequently overlooks instance-level prompt variations and their implications on subjective evaluations. To address these shortcomings, we introduce ProSA, a framework designed to evaluate and comprehend prompt sensitivity in LLMs. ProSA incorporates a novel sensitivity metric, PromptSensiScore, and leverages decoding confidence to elucidate underlying mechanisms. Our extensive study, spanning multiple tasks, uncovers that prompt sensitivity fluctuates across datasets and models, with larger models exhibiting enhanced robustness. We observe that few-shot examples can alleviate this sensitivity issue, and subjective evaluations are also susceptible to prompt sensitivities, particularly in complex, reasoning-oriented tasks. Furthermore, our findings indicate that higher model confidence correlates with increased prompt robustness. We believe this work will serve as a helpful tool in studying prompt sensitivity of LLMs. The project is released at: https://github.com/open-compass/ProSA .

• The paper introduces the PromptSensiScore to quantify how prompt variations impact LLM outputs.
• It demonstrates that few-shot learning and higher decoding confidence effectively reduce prompt sensitivity in complex tasks.
• The framework’s insights enable improved prompt design for enhanced reliability in diverse LLM applications.

Assessing and Understanding the Prompt Sensitivity of LLMs: A Summary of ProSA

The paper "ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs" explores the intricate issue of prompt sensitivity in LLMs. The authors introduce ProSA, a comprehensive framework designed to evaluate and understand how slight variations in prompts can lead to significant performance differences in LLMs. This paper highlights the importance of prompt design in the effective deployment of LLMs across diverse tasks and applications.

Introduction

LLMs have emerged as powerful tools capable of tackling a wide array of tasks. However, the performance of these models is notably sensitive to the prompts used, posing challenges for consistent assessment and user satisfaction. The paper identifies a gap in existing research, which often focuses on dataset-level evaluations while neglecting instance-level nuances and subjective evaluations of LLM performance. To address these deficiencies, the authors propose ProSA, a framework that emphasizes the understanding of prompt sensitivity at an instance level.

Methodology

ProSA introduces a novel metric, the PromptSensiScore (PSS), to quantify prompt sensitivity. PSS measures the average discrepancy in an LLM's responses when faced with different semantic variants of the same prompt. Additionally, the framework employs the concept of decoding confidence to explore the underlying mechanisms driving prompt sensitivity. The paper covers multiple tasks, including logical reasoning, coding, and general language abilities, to provide a comprehensive analysis of prompt sensitivity across different domains.

Experimental Findings

The experiments reveal several critical insights:

1. Variability in Prompt Sensitivity: Prompt sensitivity varies significantly across datasets and models. Notably, larger LLMs like Llama3-70B-Instruct demonstrate greater robustness to prompt changes compared to smaller models.
2. Effect of Few-Shot Learning: Incorporating few-shot learning examples is shown to reduce prompt sensitivity across models, particularly in complex reasoning tasks. This effect is more pronounced in larger models, suggesting they better leverage few-shot examples.
3. Correlation with Decoding Confidence: A key finding is the correlation between higher model confidence and increased prompt robustness. This suggests that a model's sensitivity to prompts is reflective of its confidence in generating outputs.
4. Subjective Evaluations: ProSA employs subjective evaluation benchmarks such as LC AlpacaEval 2.0 and Arena Hard Auto to assess LLMs' prompt sensitivity. The results indicate that LLMs handle straightforward prompts with high resilience but struggle with more complex and reasoning-intensive queries.
5. Category-Specific Sensitivity: The paper categorizes prompts to examine category-specific sensitivity. LLMs exhibit greater robustness in domains with well-established knowledge bases, such as IT troubleshooting, while showing heightened sensitivity in creative or coding tasks.

Implications and Future Directions

The findings hold significant implications for the design and deployment of LLMs. Understanding prompt sensitivity at an instance level can aid in developing more reliable performance assessments and improving the user experience. The correlation between prompt sensitivity and decoding confidence opens avenues for enhancing LLM robustness through targeted model training and tuning.

In the future, research can extend to exploring the impact of incorporating a larger number of few-shot examples and examining how model confidence can be systematically increased to reduce prompt sensitivity. Additionally, investigating the fundamental factors contributing to prompt sensitivity can lead to the development of LLMs with inherent robustness to prompt variations.

Conclusion

ProSA provides a valuable framework for assessing and understanding the prompt sensitivity of LLMs, uncovering critical insights into how these models interact with variable prompts. By introducing precise metrics like the PromptSensiScore and leveraging decoding confidence, the paper advances the field's understanding of prompt sensitivity, paving the way for more robust and user-aligned LLMs.