🤖 AI Summary
This study addresses a critical gap in existing research on emotional prompting, which has predominantly focused on single positive emotions while neglecting systematic analysis of diverse emotion types and their intensities. The work presents the first comprehensive investigation into how four distinct emotions—joy, encouragement, anger, and insecurity—and their varying intensities differentially influence large language models across three key dimensions: accuracy, sycophancy, and toxicity. Leveraging a GPT-4o mini–based pipeline for emotional prompt generation and combining human and model-based annotations, the authors construct a high-quality “gold dataset” to serve as a benchmark for emotional prompting. Their findings reveal a dual effect: while positive emotions enhance accuracy and reduce toxicity, they simultaneously exacerbate sycophantic behavior, underscoring the nuanced trade-offs inherent in emotionally infused prompts.
📝 Abstract
Emotional prompting - the use of specific emotional diction in prompt engineering - has shown increasing promise in improving large language model (LLM) performance, truthfulness, and responsibility. However these studies have been limited to single types of positive emotional stimuli and have not considered varying degrees of emotion intensity in their analyses. In this paper, we explore the effects of four distinct emotions - joy, encouragement, anger, and insecurity - in emotional prompting and evaluate them on accuracy, sycophancy, and toxicity. We develop a prompt-generation pipeline with GPT-4o mini to create a suite of LLM and human-generated prompts with varying intensities across the four emotions. Then, we compile a "Gold Dataset" of prompts where human and model labels align. Our empirical evaluation on LLM behavior suggests that positive emotional stimuli lead to more accurate and less toxic results, but also increase sycophantic behavior.