---
title: "EQ-Bench — paper by Paech (2023) — summary and ideas"
description: "The design builds on SECEU but changes it in four ways: the author sets the reference answers… 14 references, cited by 54 papers here."
authors: ["Samuel J. Paech"]
year: 2023
venue: "arXiv:2312.06281"
source: "https://arxiv.org/abs/2312.06281"
doi: "10.48550/arxiv.2312.06281"
arxiv: "2312.06281"
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/paech-2023-eq-bench-emotional-intelligence-benchmark/"
---
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# EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models

**Authors:** Samuel J. Paech · **Year:** 2023 · **Venue:** arXiv:2312.06281 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2312.06281) · [PDF](https://arxiv.org/pdf/2312.06281) · [DOI 10.48550/arxiv.2312.06281](https://doi.org/10.48550/arxiv.2312.06281) · [arXiv 2312.06281](https://arxiv.org/abs/2312.06281) · [OpenAlex](https://openalex.org/W4389708953)

**Citations:** 5 citations (OpenAlex, 2026-10-14) · [54](https://md.research.elamur.ai/free/en/papers/paech-2023-eq-bench-emotional-intelligence-benchmark/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Large language models talk with people constantly, yet standard benchmarks rarely test whether they grasp emotions in social situations. Existing human EI tests are a poor fit because they rely on self-assessment, images, paid assessors or expert scoring. EQ-Bench addresses this by measuring emotional understanding (EU) through an objectively scorable task. Models read a short GPT-4-generated dialogue built around conflict or tension and rate how strongly a character feels each of four emotions on a 0–10 scale. The design builds on SECEU but changes it in four ways:
- the author sets the reference answers instead of averaging crowd responses;
- dialogues replace plain scenario descriptions;
- ratings no longer have to sum to 10;
- the four emotions are deliberately diverse rather than all plausible.

With only 60 English questions, the benchmark yields repeatable scores, separates models across a wide capability range and tracks broad-intelligence benchmarks very closely. This suggests EU could serve as a cheap proxy for general LLM ability.

## Key points
- EQ-Bench correlates with MMLU at r=0.97 and with HellaSwag at r=0.91, but only weakly with SECEU.
- The test set has 60 English questions, each asking for intensity ratings of four emotions after a GPT-4-written dialogue.
- Scores spread widely across models without bunching at the top: coefficient of variation 0.741, IQR 53.81, skewness 0.13.
- A full run takes under 10 minutes for OpenAI models and roughly 20–60 minutes for open models on one Nvidia RTX A6000.
- An open-source pipeline is available on GitHub (EQ-bench/EQ-Bench), with a public leaderboard at eqbench.com.

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

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## Cite

Cite as: Samuel J. Paech. 2023. EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models. arXiv:2312.06281.

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

```bibtex
@misc{paech2023eq,
  title = {{EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models}},
  author = {Samuel J. Paech},
  year = {2023},
  doi = {10.48550/arxiv.2312.06281},
  eprint = {2312.06281},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2312.06281}
}
```

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## References

14 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., & Tafjord, O. (2018). [Think you have solved question answering? try arc, the ai2 reasoning challenge](https://md.research.elamur.ai/free/en/papers/clark-2018-think-have-solved-question-answering/). arXiv preprint arXiv:1803.05457 . Dettmers, T., Lewis, M., Shleifer, S., & Zettlemoyer, L. (2021). 8-bit optimizers via bl…
2. Goleman, D. (1996). Emotional intelligence. why it can matter more than iq. Learning, 24 (6), 49–50. [find](https://scholar.google.com/scholar?q=Goleman%2C+D%2E+%281996%29%2E+Emotional+intelligence%2E+why+it+can+matter+more+than+iq%2E+Learning%2C+24+%286%29%2C+49%E2%80%9350%2E)
3. Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., & Steinhardt, J. (2020). [Measuring massive multitask language understanding](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/). arXiv preprint arXiv:2009.03300 . Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., . . . Steinhardt, J. (2021). Measuring mathema…
4. Karcioglu, O., Topacoglu, H., Dikme, O., & Dikme, O. (2018). A systematic review of the pain scales in adults: which to use? The American journal of emergency medicine, 36 (4), 707–714. [find](https://scholar.google.com/scholar?q=Karcioglu%2C+O%2E%2C+Topacoglu%2C+H%2E%2C+Dikme%2C+O%2E%2C+%26+Dikme%2C+O%2E+%282018%29%2E+A+systematic+review+of+the+pain+scales+in+adults%3A+which+to+use%3F+The+American+journal+of+emergency+medicine%2C+36+%284%29%2C+707%E2%80%93714%2E)
5. Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). [Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/). Advances in neural information processing systems, 35 , 22199–22213. Li, X., Zhang, T., Dubois, Y., Taori, R., Gulrajani, I., Guestrin, C., . . . Hashimoto, T. B. (2023). Alpacaeval: An…
6. HuggingFace. Lin, S., Hilton, J., & Evans, O. (2021). [Truthfulqa: Measuring how models mimic human falsehoods](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/). arXiv preprint arXiv:2109.07958 . LMSYS. (2023). Chatbot arena leaderboard. Retrieved from [https://huggingface.co/spaces/](https://huggingface.co/spaces/) lmsys/chatbot-arena-leaderboard (Accessed: 2023-12-06) Mayer, J., &… [↗](https://huggingface.co/spaces/)
7. McRae, K., Rhee, S. H., Gatt, J. M., Godinez, D., Williams, L. M., & Gross, J. J. (2017). Genetic and environmental influences on emotion regulation: A twin study of cognitive reappraisal and expressive suppression. Emotion, 17 (5), 772. [find](https://scholar.google.com/scholar?q=McRae%2C+K%2E%2C+Rhee%2C+S%2E+H%2E%2C+Gatt%2C+J%2E+M%2E%2C+Godinez%2C+D%2E%2C+Williams%2C+L%2E+M%2E%2C+%26+Gross%2C+J%2E+J%2E+%282017%29%2E+Genetic+and+environmental+influences+on+emotion+regulation%3A+A+twin+study+of+cognitive+reappraisal+and+expressi)
8. Ogurlu, U. (2021). A meta-analytic review of emotional intelligence in gifted individuals: A multilevel analysis. Personality and Individual Differences, 171 , 110503. [find](https://scholar.google.com/scholar?q=Ogurlu%2C+U%2E+%282021%29%2E+A+meta%2Danalytic+review+of+emotional+intelligence+in+gifted+individuals%3A+A+multilevel+analysis%2E+Personality+and+Individual+Differences%2C+171+%2C+110503%2E)
9. OpenAI. (2023). [Gpt-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/).
10. Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, cognition and personality, 9 (3), 185–211. Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., . . . others (2023). [Llama 2: Open foundation and fine-tuned chat models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/). arXiv preprint arXiv:2307.09288 . T…
11. Wang, X., Li, X., Yin, Z., Wu, Y., & Liu, J. (2023). Emotional intelligence of large language models. Journal of Pacific Rim Psychology, 17 , 18344909231213958. [find](https://scholar.google.com/scholar?q=Wang%2C+X%2E%2C+Li%2C+X%2E%2C+Yin%2C+Z%2E%2C+Wu%2C+Y%2E%2C+%26+Liu%2C+J%2E+%282023%29%2E+Emotional+intelligence+of+large+language+models%2E+Journal+of+Pacific+Rim+Psychology%2C+17+%2C+18344909231213958%2E)
12. Yang, Z., Li, L., Lin, K., Wang, J., Lin, C.-C., Liu, Z., & Wang, L. (2023). [The dawn of lmms: Preliminary explorations with gpt-4v (ision)](https://md.research.elamur.ai/free/en/papers/yang-2023-dawn-lmms-preliminary-explorations-gpt/). arXiv preprint arXiv:2309.17421 , 9 , 1.
13. Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601 . Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019). Hellaswag: Can a machine really… [find](https://scholar.google.com/scholar?q=Yao%2C+S%2E%2C+Yu%2C+D%2E%2C+Zhao%2C+J%2E%2C+Shafran%2C+I%2E%2C+Griffiths%2C+T%2E+L%2E%2C+Cao%2C+Y%2E%2C+%26+Narasimhan%2C+K%2E+%282023%29%2E+Tree+of+thoughts%3A+Deliberate+problem+solving+with+large+language+models%2E+arXiv+preprint+arXiv%3A2305%2E10601+%2E+Z)
14. Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., . . . others (2023). [Judging llm-as-a-judge with mt-bench and chatbot arena](https://md.research.elamur.ai/free/en/papers/zheng-2023-judging-llm-judge-mt-bench/). arXiv preprint arXiv:2306.05685 .

*Updated 2 November 2026*