1 citations · 3 across the 20 of their papers we have counts for
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Class-Structure Preservation Beats Diversity: A Comprehensive Benchmark of Text Augmentation Methods for Imbalanced Text Classification
Keito Inoshita
With the rapid advancement of large language models (LLMs), generative data augmentation has attracted considerable attention for imbalanced text classification in natural language…
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,…
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…
Nationality and Region Prediction from Names: A Comparative Study of Neural Models and Large Language Models
Keito Inoshita
Predicting nationality from personal names has practical value in marketing, demographic research, and genealogical studies. Conventional neural models learn statistical correspond…
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…
World model inspired sarcasm reasoning with large language model agents
Keito Inoshita, Shinnosuke Mizuno
Sarcasm understanding is a challenging problem in natural language processing, as it requires capturing the discrepancy between the surface meaning of an utterance and the speaker'…
Persona-Based Synthetic Data Generation Using Multi-Stage Conditioning with Large Language Models for Emotion Recognition
Keito Inoshita, Rushia Harada
In the field of emotion recognition, the development of high-performance models remains a challenge due to the scarcity of high-quality, diverse emotional datasets. Emotional expre…
A Multi-Agent Probabilistic Inference Framework Inspired by Kairanban-Style CoT System with IdoBata Conversation for Debiasing
Takato Ueno, Keito Inoshita
Japan's kairanban culture and idobata conversations have long functioned as traditional communication practices that foster nuanced dialogue among community members and contribute…
Evaluation of the Automated Labeling Method for Taxonomic Nomenclature Through Prompt-Optimized Large Language Model
Keito Inoshita, Kota Nojiri, Haruto Sugeno +1
Scientific names of organisms consist of a genus name and a species epithet, with the latter often reflecting aspects such as morphology, ecology, distribution, and cultural backgr…