# NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli

arXiv: https://arxiv.org/abs/2405.02814 · HTML: https://mlsanity.com/paper/2405.02814/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 5 May 2024.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Instruction Induction: From Few Examples to Natural Language Task Descriptions](https://mlsanity.com/paper/2205.10782/) (2022)
- [Emotional Intelligence of Large Language Models](https://mlsanity.com/paper/2307.09042/) (2023)
- [How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization?](https://mlsanity.com/paper/2306.01248/) (2023)
- [A Survey on Data Augmentation in Large Model Era](https://mlsanity.com/paper/2401.15422/) (2024)

## Built on by (most cited first, 18 of 18)

- [Self-Supervised Prompt Optimization](https://mlsanity.com/paper/2502.06855/) (2025)
- [Valence-Arousal Subspace in LLMs: Circular Emotion Geometry and Multi-Behavioral Control](https://mlsanity.com/paper/2604.03147/) (2026)
- [Decoding Emotion in the Deep: A Systematic Study of How LLMs Represent, Retain, and Express Emotion](https://mlsanity.com/paper/2510.04064/) (2025)
- [StressPrompt: Does Stress Impact Large Language Models and Human Performance Similarly?](https://mlsanity.com/paper/2409.17167/) (2024)
- [From Rational Answers to Emotional Resonance: The Role of Controllable Emotion Generation in Language Models](https://mlsanity.com/paper/2502.04075/) (2025)
- [Revise, Reason, and Recognize: LLM-Based Emotion Recognition via Emotion-Specific Prompts and ASR Error Correction](https://mlsanity.com/paper/2409.15551/) (2024)
- [FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning](https://mlsanity.com/paper/2410.19727/) (2024)
- [PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models](https://mlsanity.com/paper/2505.16307/) (2025)
- [Artificial Impressions: Evaluating Large Language Model Behavior Through the Lens of Trait Impressions](https://mlsanity.com/paper/2510.08915/) (2025)
- [Remedying Target-Domain Astigmatism for Cross-Domain Few-Shot Object Detection](https://mlsanity.com/paper/2603.18541/) (2026)
- [Do Emotions Influence Moral Judgment in Large Language Models?](https://mlsanity.com/paper/2604.19125/) (2026)
- [FreakOut-LLM: The Effect of Emotional Stimuli on Safety Alignment](https://mlsanity.com/paper/2604.04992/) (2026)
- [TEMPER: Testing Emotional Perturbation in Quantitative Reasoning](https://mlsanity.com/paper/2604.07801/) (2026)
- [Understanding Conversational Patterns in Multi-agent Programming: A Case Study on Fibonacci Game Development](https://mlsanity.com/paper/2605.24138/) (2026)
- [Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration](https://mlsanity.com/paper/2606.18259/) (2026)
- [How Human is AI? Examining the Impact of Emotional Prompts on Artificial and Human and Responsiveness](https://mlsanity.com/paper/2601.05104/) (2026)
- [Do Emotions in Prompts Matter? Effects of Emotional Framing on Large Language Models](https://mlsanity.com/paper/2604.02236/) (2026)
- [The Role of Emotional Stimuli and Intensity in Shaping Large Language Model Behavior](https://mlsanity.com/paper/2604.07369/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have become integral to a wide spectrum of applications, ranging from traditional computing tasks to advanced artificial intelligence (AI) applications. This widespread adoption has spurred extensive research into LLMs across various disciplines, including the social sciences. Notably, studies have revealed that LLMs possess emotional intelligence, which can be further developed through positive emotional stimuli. This discovery raises an intriguing question: can negative emotions similarly influence LLMs, potentially enhancing their performance? In response to this question, we introduce NegativePrompt, a novel approach underpinned by psychological principles, involving ten specifically designed negative emotional stimuli. We embark on rigorous experimental evaluations of five LLMs including Flan-T5-Large, Vicuna, Llama 2, ChatGPT, and GPT-4, across a set of 45 tasks. The results are revealing: NegativePrompt markedly enhances the performance of LLMs, evidenced by relative improvements of 12.89% in Instruction Induction tasks and 46.25% in BIG-Bench tasks. Moreover, we conduct attention visualization experiments to decipher the underlying mechanisms of NegativePrompt's influence. Our research contributes significantly to the understanding of LLMs and emotion interaction, demonstrating the practical efficacy of NegativePrompt as an emotion-driven method and offering novel insights for the enhancement of LLMs in real-world applications. The code is available at https://github.com/wangxu0820/NegativePrompt.