# EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models

arXiv: https://arxiv.org/abs/2312.06281 · HTML: https://mlsanity.com/paper/2312.06281/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://mlsanity.com/paper/2309.17421/) (2023)
- [8-bit Optimizers via Block-wise Quantization](https://mlsanity.com/paper/2110.02861/) (2021)
- [Emotional Intelligence of Large Language Models](https://mlsanity.com/paper/2307.09042/) (2023)

## Built on by (most cited first, 30 of 80)

- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs](https://mlsanity.com/paper/2407.01082/) (2024)
- [Ministral 3](https://mlsanity.com/paper/2601.08584/) (2026)
- [Reinforcement Learning with Rubric Anchors](https://mlsanity.com/paper/2508.12790/) (2025)
- [WritingBench: A Comprehensive Benchmark for Generative Writing](https://mlsanity.com/paper/2503.05244/) (2025)
- [AgentInstruct: Toward Generative Teaching with Agentic Flows](https://mlsanity.com/paper/2407.03502/) (2024)
- [Jointly Reinforcing Diversity and Quality in Language Model Generations](https://mlsanity.com/paper/2509.02534/) (2025)
- [Self-Exploring Language Models: Active Preference Elicitation for Online Alignment](https://mlsanity.com/paper/2405.19332/) (2024)
- [Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?](https://mlsanity.com/paper/2407.21792/) (2024)
- [Reward-Robust RLHF in LLMs](https://mlsanity.com/paper/2409.15360/) (2024)
- [EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models](https://mlsanity.com/paper/2502.04424/) (2025)
- [Online Preference Alignment for Language Models via Count-based Exploration](https://mlsanity.com/paper/2501.12735/) (2025)
- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families](https://mlsanity.com/paper/2412.06540/) (2024)
- [Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models](https://mlsanity.com/paper/2505.02847/) (2025)
- [Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision](https://mlsanity.com/paper/2509.14234/) (2025)
- [Beyond the Surface: Enhancing LLM-as-a-Judge Alignment with Human via Internal Representations](https://mlsanity.com/paper/2508.03550/) (2025)
- [HEART: A Unified Benchmark for Assessing Humans and LLMs in Emotional Support Dialogue](https://mlsanity.com/paper/2601.19922/) (2026)
- [Are Language Models Consequentialist or Deontological Moral Reasoners?](https://mlsanity.com/paper/2505.21479/) (2025)
- [Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models](https://mlsanity.com/paper/2602.04649/) (2026)
- [The Good, The Bad, and Why: Unveiling Emotions in Generative AI](https://mlsanity.com/paper/2312.11111/) (2023)
- [Evaluating Text Creativity across Diverse Domains: A Dataset and Large Language Model Evaluator](https://mlsanity.com/paper/2505.19236/) (2025)
- [StoryScope: Investigating idiosyncrasies in AI fiction](https://mlsanity.com/paper/2604.03136/) (2026)
- [Frankentext: Stitching random text fragments into long-form narratives](https://mlsanity.com/paper/2505.18128/) (2025)
- [Hermes 4 Technical Report](https://mlsanity.com/paper/2508.18255/) (2025)
- [Unveiling the Cognitive Compass: Theory-of-Mind-Guided Multimodal Emotion Reasoning](https://mlsanity.com/paper/2602.00971/) (2026)
- [CML-Bench: A Framework for Evaluating and Enhancing LLM-Powered Movie Scripts Generation](https://mlsanity.com/paper/2510.06231/) (2025)
- [Understanding DeepResearch via Reports](https://mlsanity.com/paper/2510.07861/) (2025)
- [Escaping the Verifier: Learning to Reason via Demonstrations](https://mlsanity.com/paper/2511.21667/) (2025)
- [Bielik 11B v3: Multilingual Large Language Model for European Languages](https://mlsanity.com/paper/2601.11579/) (2025)

## Abstract (arXiv)

We introduce EQ-Bench, a novel benchmark designed to evaluate aspects of emotional intelligence in Large Language Models (LLMs). We assess the ability of LLMs to understand complex emotions and social interactions by asking them to predict the intensity of emotional states of characters in a dialogue. The benchmark is able to discriminate effectively between a wide range of models. We find that EQ-Bench correlates strongly with comprehensive multi-domain benchmarks like MMLU (Hendrycks et al., 2020) (r=0.97), indicating that we may be capturing similar aspects of broad intelligence. Our benchmark produces highly repeatable results using a set of 60 English-language questions. We also provide open-source code for an automated benchmarking pipeline at https://github.com/EQ-bench/EQ-Bench and a leaderboard at https://eqbench.com