1 citations · 2 across the 9 of their papers we have counts for
15 papers
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,…
Cognitive-Causal Multi-Task Learning with Psychological State Conditioning for Assistive Driving Perception
Keito Inoshita, Nobuhiro Hayashida, Akira Imanishi
Multi-task learning for advanced driver assistance systems requires modeling the complex interplay between driver internal states and external traffic environments. However, existi…
Does AI Homogenize Student Thinking? A Multi-Dimensional Analysis of Structural Convergence in AI-Augmented Essays
Keito Inoshita, Michiaki Omura, Tsukasa Yamanaka +2
While AI-assisted writing has been widely reported to improve essay quality, its impact on the structural diversity of student thinking remains unexplored. Analyzing 6,875 essays a…
Multi-Agent Large Language Model Based Emotional Detoxification Through Personalized Intensity Control for Consumer Protection
Keito Inoshita
In the attention economy, sensational content exposes consumers to excessive emotional stimulation, hindering calm decision-making. This study proposes Multi-Agent LLM-based Emotio…
Argument Rarity-based Originality Assessment for AI-Assisted Writing
Keito Inoshita, Michiaki Omura, Tsukasa Yamanaka +2
This study proposes Argument Rarity-based Originality Assessment (AROA), a framework for automatically evaluating argumentative originality in student essays. AROA defines original…
Who Does This Name Remind You of ? Nationality Prediction via Large Language Model Associative Memory
Keito Inoshita
Large language models (LLMs) possess extensive world knowledge, yet methods for effectively eliciting this knowledge remain underexplored. Nationality and region prediction tasks r…