Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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Be Rude, Get Smart! ChatGPT-4o Prefers Your Sass, Not Your 'Pleaease!'

This study found that impolite prompts consistently led to higher accuracy in ChatGPT-4o on multiple-choice questions, outperforming polite prompts. The research utilized a relatively small dataset of 50 base questions, each rewritten into five politeness variants, and primarily tested only one LLM. These findings suggest that newer LLMs may respond differently to tonal variation than previously observed.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Small Dataset Size

The study used only 50 base multiple-choice questions, rewritten into 250 variants. This small dataset limits the generalizability of the findings across a wider range of tasks or knowledge domains.

Limited LLM Scope

Experiments primarily relied on ChatGPT-4o. The paper acknowledges that different LLM architectures and training corpora may respond differently, and thus, the findings may not apply to other models without further validation.

Narrow Performance Metric

The evaluation focused solely on accuracy in a multiple-choice setting. It did not assess other important qualities of LLM performance such as fluency, reasoning, coherence, or helpfulness.

Constrained Politeness Operationalization

The definition of 'politeness' and 'rudeness' relied on specific linguistic cues (prefixes), which may not encompass the full sociolinguistic spectrum of tone or account for cross-cultural differences. This could lead to a simplified understanding of how politeness manifests.

Ethical Implications of Findings

The authors acknowledge that the finding (rude prompts yielding better results) could encourage the deployment of hostile or toxic interfaces, leading to negative user experience and harmful communication norms, which is a significant concern for responsible AI development.

Rating Explanation

The paper presents interesting, counterintuitive findings regarding prompt politeness and LLM accuracy. However, its generalizability is limited by a small dataset, reliance on a single LLM (ChatGPT-4o) for most experiments, and a narrow definition of politeness. It's a good preliminary study, but needs broader validation.

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