---
title: "At Your Own PACE — paper by Lyu et al. (2026)"
description: "The authors build a four-part evaluation stack: PACE Taxonomy breaks LLM EQ down into four dimensions… 83.6% agreement. 35 references."
authors: ["Lei Lyu", "Shengling Wang", "Ke Chao", "Yichao Wei"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.623/"
doi: "10.18653/v1/2026.findings-acl.623"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lyu-2026-your-own-pace-causal-framework/"
---
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# At Your Own PACE: A Causal Framework for Evaluating EQ in LLMs

**Authors:** Lei Lyu, Shengling Wang, Ke Chao and Yichao Wei · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.623/) · [PDF](https://aclanthology.org/2026.findings-acl.623.pdf) · [DOI 10.18653/v1/2026.findings-acl.623](https://doi.org/10.18653/v1/2026.findings-acl.623) · [OpenAlex](https://openalex.org/W7166830557)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Current ways of measuring emotional intelligence (EQ) in LLMs have two problems. Some borrow human psychometric scales, which focus on managing one's own feelings rather than helping other people. Others test isolated skills, mostly emotion recognition, without combining them into one score.

The authors build a four-part evaluation stack:
- **PACE Taxonomy** breaks LLM EQ down into four dimensions: Perceptive Insight (P) and Adaptive Attunement (A), along with Constructive Conviction (C) and Empathic Guidance (E). Together these cover both diagnosing and repairing a user's emotional state.
- **Causal-PACE** places the dimensions in a causal graph. It uses counterfactual intervention to remove confounding between them, which produces causally derived weights for a composite EQ score.
- **PACE-AB** puts this into practice with four specialist judge agents and a Chief Arbitrator that combines their scores.
- **PACE-2700** is the benchmark: a bilingual Chinese–English set of first-person prompts. It carries dimension annotations and human preference labels collected in a double-blind arena.

The causally weighted scores agree with human preferences 89.31% of the time. The automated board reaches 83.6% agreement, beating single-LLM judges. The work gives developers one human-aligned EQ measure, plus preference data usable for RLHF or DPO.

## Key points
- **PACE-2700:** 2,700 instructions covering 5 emotional-social scenario types (venting/self-doubt, interpersonal conflict, work/academic stress, diary reflections, advice-seeking). The data is publicly released. Most English prompts were written by non-native speakers, which the authors list as a limitation.
- **PACE-AB versus human judgments:** 83.6% accuracy, Cohen's κ = 0.67, Spearman ρ = 0.91, Kendall τ = 0.73. The best single-model judge (GPT 4) reached 78.4%. Removing the Chief Arbitrator lowers accuracy to 68.6%.
- **Causal weights:** Perceptive Insight is the dominant driver. Empathic Guidance and Constructive Conviction add to it, while Adaptive Attunement matters little. The causal ranking matches human battle rankings closely (ρ = 0.95, τ = 0.83).
- **Model profiles:** Gemini 2.5 Pro leads on P and A, while Claude Sonnet 4.5 leads on C and E. The Claude 4.5 models score the top EQ value of 89. Few models excel on all four dimensions.
- **EQ versus IQ:** EQ correlates only weakly with Tracking AI IQ scores (ρ ≈ 0.28, τ ≈ 0.15). For example, Grok 4 ranks 2nd in IQ but 7th in EQ.

*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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- [Beijing Normal University](https://md.research.elamur.ai/free/en/universities/beijing-normal-university/) — University, Beijing, China · 22 works · 3,177 citations

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## Cite

Cite as: Lei Lyu, Shengling Wang, Ke Chao and Yichao Wei. 2026. At Your Own PACE: A Causal Framework for Evaluating EQ in LLMs. Findings of the Association for Computational Linguistics: ACL 2026.

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

```bibtex
@inproceedings{lyu2026at,
  title = {{At Your Own PACE: A Causal Framework for Evaluating EQ in LLMs}},
  author = {Lei Lyu, Shengling Wang, Ke Chao and Yichao Wei},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.623},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.623}
}
```

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*Updated 2 November 2026*