CAREBench: Evaluating LLMs' Emotion Understanding by Assessing Cognitive Appraisal Reasoning
The article introduces CAREBench, a new benchmark designed to evaluate the emotion understanding capabilities of large language models (LLMs). It highlights the limitations of existing evaluation methods and proposes a process-level evaluation framework based on cognitive appraisal reasoning. The findings suggest that while some LLMs perform well in certain tasks, they struggle with understanding human emotional complexity.
- ▪CAREBench is the first benchmark with complete inferential chain annotations for evaluating LLMs' emotion understanding.
- ▪The study reveals that stronger models can match or exceed human performance in specific tasks but fall short in appraisal reasoning and positive emotion recognition.
- ▪Current LLMs have not fully internalized the cognitive mechanisms necessary to capture human emotional diversity.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17176 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.17176 (cs) [Submitted on 16 May 2026] Title:CAREBench: Evaluating LLMs' Emotion Understanding by Assessing Cognitive Appraisal Reasoning Authors:Zhaoyue Sun, Hainiu Xu, Andero Uusberg, James J. Gross, Petr Slovak, Yulan He View a PDF of the paper titled CAREBench: Evaluating LLMs' Emotion Understanding by Assessing Cognitive Appraisal Reasoning, by Zhaoyue Sun and 4 other authors View PDF HTML (experimental) Abstract:Emotion understanding is a core capability for LLMs to interact effectively with humans, yet existing evaluation paradigms rely on discrete emotion label prediction and fail to capture the cognitive processes underlying emotion generation.
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