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University of Reading, Nanyang Technological University
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Emotion classifiers can now provide explanations that are not just post hoc but are grounded in definitional semantics, ensuring transparency and auditability.
A comprehensive taxonomy reveals critical failure modes in LLM reasoning, exposing vulnerabilities that could hinder their deployment in real-world applications.
Standard multimodal fusion can hurt performance in emotion recognition, but this new approach knows when to drop modalities, leading to state-of-the-art results.
