Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance (2402.14531v1)

Abstract: We investigate the impact of politeness levels in prompts on the performance of LLMs. Polite language in human communications often garners more compliance and effectiveness, while rudeness can cause aversion, impacting response quality. We consider that LLMs mirror human communication traits, suggesting they align with human cultural norms. We assess the impact of politeness in prompts on LLMs across English, Chinese, and Japanese tasks. We observed that impolite prompts often result in poor performance, but overly polite language does not guarantee better outcomes. The best politeness level is different according to the language. This phenomenon suggests that LLMs not only reflect human behavior but are also influenced by language, particularly in different cultural contexts. Our findings highlight the need to factor in politeness for cross-cultural natural language processing and LLM usage.

• The paper demonstrates that optimal prompt politeness levels vary by language, significantly affecting summarization quality and bias detection.
• It employs systematic experiments with a spectrum of politeness levels to reveal nuanced, non-linear impacts on language understanding benchmarks.
• The study highlights that extreme impoliteness degrades model performance while overly polite prompts do not consistently enhance results, urging culturally aware prompt design.

The Influence of Prompt Politeness on LLM Performance Across Different Languages

Introduction

The impact of prompt politeness on the performance of LLMs has been an area of growing interest within the field of NLP. This paper investigates the effect of varying levels of prompt politeness on LLMs across English, Chinese, and Japanese, aiming to understand how cultural factors might influence the efficacy of these computational models. By meticulously designing prompts that range from highly polite to highly impolite and conducting experiments across several tasks including summarization, language understanding benchmarks, and stereotypical bias detection, this research sheds light on the complex relationship between language, culture, and machine understanding.

Experiment Design and Contributions

Politeness in Context

The premises of this paper are rooted in the diversity of politeness and respect expressions across languages, reflecting the deep cultural nuances inherent in human communication. Recognized methods of expressing politeness in English, Chinese, and Japanese present varying levels of complexity and societal implications, which could potentially impact the processing capabilities of LLMs trained on data imbibed with these cultural nuances.

Methodology

To conduct this exploratory analysis, the researchers crafted a spectrum of prompts based on defined politeness levels across the three languages in question. These prompts were then utilized in a series of experiments aimed at evaluating the LLMs' performance in summarization tasks, multitask language understanding benchmarks dubbed JMMLU for Japanese, and detection of stereotypical biases.

Main Findings

Summarization Results

The paper found that LLMs often generate poor quality outputs with impolite prompts, whereas overly polite language does not consistently enhance performance. Remarkably, the optimal level of politeness for eliciting the best performance varies by language, emphasizing the importance of cultural context in LLM interactions.

Language Understanding Benchmarks

The evaluation on language understanding benchmarks revealed a nuanced relationship between prompt politeness and model performance. While the trend was not universally linear, a notable observation across all languages was a decrease in model efficacy with highly impolite prompts. However, the tolerance levels for politeness varied, with each language demonstrating unique sensitivities that reflect its cultural idiosyncrasies.

Stereotypical Bias Detection

The investigation into how prompt politeness impacts the expression of stereotypical biases by LLMs offered intriguing insights. Generally, models were found to exhibit more pronounced biases under extreme politeness levels, likely mirroring the human tendency to express uninhibited views in comfortable communication environments. The degree of bias also varied with the level of impoliteness, suggesting a complex interplay between cultural norms of respect and computational representations of bias.

Implications and Future Directions

This research underscores the significance of considering cultural nuances when designing prompts for LLMs. The distinct influence of politeness on LLM performance across languages suggests that cultural context is an important factor in natural language understanding systems. It points towards the necessity for more culturally aware datasets and model training processes, proposing a broader scope for the incorporation of cultural sensitivity in the development of AI systems.

Limitations and Ethics

Acknowledging limitations related to prompt diversity, task configuration, and language selection, the researchers advocate for a broader exploration into other languages and contexts. Furthermore, ethical considerations around the potential manipulation of LLM output through prompt engineering are duly noted, highlighting the importance of responsible AI development and deployment.

Conclusion

This paper brings to the fore the intricate relationship between language, culture, and artificial intelligence, providing a foundational understanding that could significantly inform future LLM development strategies. The nuanced differences in how politeness levels affect LLM performance across English, Chinese, and Japanese serve as a vivid reminder of the complexities inherent in human languages and underscore the critical role of cultural context in the advancement of AI technologies.