most citedLLMs Capture Emotion Labels, Not Emotion Uncertainty: Distributional Analysis and Calibration of Human-LLM Judgment Gaps

1 citations · 1 across the 1 of their papers we have counts for

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2 papers

cs.CL2026★ 1 cited

LLMs Capture Emotion Labels, Not Emotion Uncertainty: Distributional Analysis and Calibration of Human-LLM Judgment Gaps

Keito Inoshita, Xiaokang Zhou, Akira Kawai +1

Human annotators frequently disagree on emotion labels, yet most evaluations of Large Language Model (LLM) emotion annotation collapse these judgments into a single gold standard,…

cs.LG2025

CIEGAD: Cluster-Conditioned Interpolative and Extrapolative Framework for Geometry-Aware and Domain-Aligned Data Augmentation

Keito Inoshita, Xiaokang Zhou, Akira Kawai +1

In practical deep learning deployment, the scarcity of data and the imbalance of label distributions often lead to semantically uncovered regions within the real-world data distrib…