---
title: "Large Language Models Understand and Can be Enhanced by"
description: "The authors propose EmotionPrompt, which appends one of 11 short motivational or pressuring… 40 references, cited by 57 papers here."
authors: ["Cheng Li", "Jindong Wang", "Yixuan Zhang", "Kaijie Zhu", "Wenxin Hou", "Jianxun Lian", "Fang Luo", "Qiang Yang", "Xing Xie"]
year: 2023
venue: "arXiv:2307.11760"
source: "https://arxiv.org/abs/2307.11760"
doi: "10.48550/arxiv.2307.11760"
arxiv: "2307.11760"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/li-2023-large-language-models-understand-enhanced/"
---
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# Large Language Models Understand and Can be Enhanced by Emotional Stimuli

**Authors:** [Cheng Li](https://md.research.elamur.ai/free/en/authors/cheng-li/), [Jindong Wang](https://md.research.elamur.ai/free/en/authors/jindong-wang/), [Yixuan Zhang](https://md.research.elamur.ai/free/en/authors/yixuan-zhang/), [Kaijie Zhu](https://md.research.elamur.ai/free/en/authors/kaijie-zhu/), Wenxin Hou, [Jianxun Lian](https://md.research.elamur.ai/free/en/authors/jianxun-lian/), Fang Luo, Qiang Yang and [Xing Xie](https://md.research.elamur.ai/free/en/authors/xing-xie/) · **Year:** 2023 · **Venue:** arXiv:2307.11760 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2307.11760) · [PDF](https://arxiv.org/pdf/2307.11760) · [DOI 10.48550/arxiv.2307.11760](https://doi.org/10.48550/arxiv.2307.11760) · [arXiv 2307.11760](https://arxiv.org/abs/2307.11760) · [OpenAlex](https://openalex.org/W4385291101)

**Citations:** 57 citations (OpenAlex, 2026-10-14) · [57](https://md.research.elamur.ai/free/en/papers/li-2023-large-language-models-understand-enhanced/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
This study asks whether large language models react to psychologically grounded emotional cues the way humans do, and whether such cues can improve their output. The authors propose EmotionPrompt, which appends one of 11 short motivational or pressuring sentences (EP01–EP11) to an ordinary instruction. The sentences draw on research about self-monitoring, Social Cognitive theory and cognitive emotion regulation. EmotionPrompt is tested automatically on 45 tasks (24 from Instruction Induction and 21 selected from BIG-Bench) using Flan-T5-Large, Vicuna, Llama 2, BLOOM, ChatGPT and GPT-4. A separate evaluation with 106 human participants rates open-ended GPT-4 answers. Adding a stimulus yields an 8.00% relative gain on Instruction Induction and 115% on BIG-Bench. Human raters report an average 10.9% improvement across performance, truthfulness and responsibility. An input-attention analysis suggests the appended sentences receive notable weight and reinforce the original instruction's representation. The result matters because it is a free, training-less prompting technique, and it invites further work connecting psychology with LLM behaviour.

## Key points
- **Setup:** 6 LLMs, 45 tasks and 11 stimuli. EP06 is a compound of EP01, EP02 and EP03. One example stimulus tells the model the task matters for the user's career.
- **Best stimuli:** EP02 performs best on Instruction Induction, beating the weakest stimulus by 6.06%. EP06 performs best on BIG-Bench. Combining several stimuli can raise scores further.
- **Relative Gain by model** (zero-shot Instruction Induction): Vicuna 9.58, Llama 2 6.00, ChatGPT 4.32, GPT-4 0.85, BLOOM 0.51, Flan-T5-Large 0.28.
  - The authors attribute the small gains of strong models partly to their high baselines.
  - Vicuna and Llama 2 share the same 13B size but show different gains, which hints that training strategy (SFT/RLHF) matters.
- **Temperature ablation** (8 tasks, 5 temperatures): the benefit grows as temperature rises. EmotionPrompt outputs also vary less with temperature than vanilla prompts, suggesting better robustness.
- **Open question:** in humans, emotional stimuli are not thought to directly improve reasoning ability. The authors leave this human–LLM divergence for future research.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) — Company, Redmond, United States · 240 works · 123,537 citations
- [Hong Kong University of Science and Technology](https://md.research.elamur.ai/free/en/universities/hong-kong-university-of-science-and-technology/) — University, Hong Kong, China · 58 works · 23,058 citations
- [Beijing Normal University](https://md.research.elamur.ai/free/en/universities/beijing-normal-university/) — University, Beijing, China · 6 works · 3,107 citations
- [Institute of Software](https://md.research.elamur.ai/free/en/labs/institute-of-software/) — Research institute, Beijing, China · 10 works · 4,807 citations
- [William & Mary](https://md.research.elamur.ai/free/en/universities/william-mary/) — University, Williamsburg, United States · 2 works · 340 citations

Also on the byline: [CAS Software](https://ror.org/027tvqf96).

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🧭 Topics (2): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 1,344 works · [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 609 works

## Cite

Cite as: Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang and Xing Xie. 2023. Large Language Models Understand and Can be Enhanced by Emotional Stimuli. arXiv:2307.11760.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{li2023large,
  title = {{Large Language Models Understand and Can be Enhanced by Emotional Stimuli}},
  author = {Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang and Xing Xie},
  year = {2023},
  doi = {10.48550/arxiv.2307.11760},
  eprint = {2307.11760},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2307.11760}
}
```

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*Updated 24 October 2026*