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The paper presents Chiaro, a new benchmark dataset for contrastive emotion recognition where two individuals experience opposing emotions from a shared event, grounded in appraisal theory. It evaluates seven LLMs and four emotion classifiers, revealing that current models fall short of human performance.
This paper compares human linguists in training, a trained linguist, and LLMs on annotating evaluative language using Appraisal theory, finding that LLMs achieve strong performance and can assist in complex annotation tasks.
This paper introduces a dose-controllable method for inducing seven psychological disorders in reinforcement learning agents by manipulating cognitive appraisal signals in an appraisal-guided PPO agent. The disorders self-organize into a two-dimensional affective space, and the framework enables modeling of both disorder induction and treatment.
This paper introduces PanicCognitivePath (PCP), a framework that combines cognitive graphs and LLMs to predict individual panic emotional arousal timing during emergencies, grounding prediction in appraisal emotion theory and demonstrating improved accuracy on the Hurricane Sandy dataset.
Introduces CAREBench, a benchmark grounded in appraisal theory to evaluate LLMs' emotion understanding through cognitive appraisal reasoning, revealing that current models struggle with reasoning and positive emotion recognition despite matching humans on some downstream tasks.