1 citations · 1 across the 3 of their papers we have counts for
3 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.CL2026
Multi-Stage Evolutionary Model Merging with Meta Data Driven Curriculum Learning for Sentiment-Specialized Large Language Modeling
Keito Inoshita, Xiaokang Zhou, Akira Kawai
The emergence of large language models (LLMs) has significantly transformed natural language processing (NLP), enabling more generalized models to perform various tasks with minima…
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…