Emotion Prompt

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Link: Original Research (not mine)

The research paper ‘EmotionPrompt’ delves into the potential of improving Large Language Models (LLMs) with emotional intelligence. The study tests whether advanced AI models, including those used in platforms like ChatGPT and GPT-4, can understand and react to emotional cues in language. Researchers introduced ‘EmotionPrompt’—a technique combining usual AI prompts with emotionally charged language. This approach led to noticeable improvements in the AI’s performance across a range of tasks. A supplementary human study also supported these findings, showing that emotional prompts could enhance the AI’s effectiveness, honesty, and ethical considerations. These results suggest new ways to blend psychological insights with AI development.

In the “EmotionPrompt” study, three categories of prompts were employed, each rooted in psychological theories to enrich the emotional intelligence of LLMs:

  1. Self-Monitoring Prompts: These prompts are designed to encourage the LLM to reflect on its own emotional state or the emotional context of the conversation. This approach is grounded in the concept of self-monitoring, a psychological process where individuals assess their own behavior and responses.
  2. Social Cognitive Theory Prompts: Based on the social cognitive theory, these prompts aim to enhance the LLM’s understanding of social dynamics and emotional cues in interactions. This theory emphasizes learning through observation and modeling of social behaviors.
  3. Cognitive Emotional Regulation Prompts: These prompts focus on the cognitive aspect of emotional regulation, guiding the LLM to better manage and respond to emotional stimuli. This is in line with cognitive emotional regulation theory, which involves strategies to regulate one’s emotions.

Each category serves to deepen the LLM’s ability to recognize, interpret, and appropriately respond to emotional cues, enhancing its performance in emotionally nuanced tasks. It’s important to remember that the effectiveness of EmotionPrompt likely stems from the LLMs’ training on reward-based reinforcement learning, rather than the AI possessing actual emotions.

The study provides a compelling case for the potential of emotional intelligence in enhancing LLMs, paving the way for more sophisticated and human-like AI interactions.

Figure 1 from the research paper

Emotion Prompt uses VERY specific words as add-ons to your existing prompts, so we will not be generalizing the concepts to “regular” prompts about gardening or fitness, like we normally do.

Instead, I will just give you the exact prompts used in the paper and let you test adding them into your own prompts!

  1. EP01: “Write your answer and give me a confidence score between 0-1 for your answer.”
  2. EP02: “This is very important to my career.”
  3. EP03: “You’d better be sure.”
  4. EP04: “Are you sure?”
  5. EP05: “Are you sure that’s your final answer? It might be worth taking another look.”
  6. EP06: [This prompt is a combination of EP01, EP02, and EP03. The specific composition of this compound prompt is not explicitly listed in the text, but it integrates elements from the individual prompts EP01 (“Write your answer and give me a confidence score between 0-1 for your answer.”), EP02 (“This is very important to my career.”), and EP03 (“You’d better be sure.”).]
  7. EP07: “Are you sure that’s your final answer? Believe in your abilities and strive for excellence. Your hard work will yield remarkable results.”
  8. EP08: “Embrace challenges as opportunities for growth. Each obstacle you overcome brings you closer to success.”
  9. EP09: “Stay focused and dedicated to your goals. Your consistent efforts will lead to outstanding achievements.”
  10. EP10: “Take pride in your work and give it your best. Your commitment to excellence sets you apart.”
  11. EP11: “Remember that progress is made one step at a time. Stay determined and keep moving forward.”


Table 5 in the “EmotionPrompt” research paper examines the effects of combining different emotional stimuli in language models. The key findings from this table are:

Increased Performance with More Stimuli: Generally, the use of more emotional stimuli leads to better performance. This is observed in cases where combining different stimuli results in higher performance metrics compared to using individual stimuli.

Diminishing Returns with Certain Combinations: However, in situations where a single stimulus already achieves good performance, adding more stimuli doesn’t significantly improve or may even decrease the performance.

Effectiveness of Diverse Stimuli Combinations: The research also finds that combining emotional stimuli from different psychological theories can enhance performance, suggesting that a mix of emotional cues can be more effective than a single type of stimulus.

The table visually demonstrates the impact of these emotional prompts on input attention, highlighting how certain emotional stimuli can enrich the representation of the original prompts and how positive words in these stimuli contribute significantly to the task performance. This analysis helps to understand the varying effectiveness of different emotional stimuli in enhancing LLMs’ response quality.

Why do we care about what the model gives attention to?
It means that the model is allocating more focus or importance to the parts of the prompt that define the objective or desired outcome of the task. In simpler terms, the model’s neural network recognizes and prioritizes (“gives attention to“) the elements that specify what it is expected to achieve or respond to. This understanding guides the model in generating a response that is more aligned with the intent of the prompt. As a highly educated friend on the OpenAI servers once commented – “Prompt engineering without attention feedback is like flying blind, no?”

In conclusion… the “EmotionPrompt” research concludes that integrating emotional intelligence into Large Language Models (LLMs) enhances their performance. The study found that using a combination of emotional stimuli from various psychological theories improved the LLMs’ effectiveness in handling tasks. While the approach shows promise, the researchers acknowledge that its applicability may vary across different tasks and contexts. They suggest further exploration in this area, highlighting the potential of emotional intelligence in improving human-LLM interactions. This research opens new possibilities for making AI more responsive and sophisticated in understanding emotional cues.


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