Norm of Word Embedding Encodes Information Gain. - Momose Oyama; Sho Yokoi; Hidetoshi Shimodaira (EMNLP, 2023)

Discovering Universal Geometry in Embeddings with ICA. - Hiroaki Yamagiwa; Momose Oyama; Hidetoshi Shimodaira (EMNLP, 2023)

Understanding Higher-Order Correlations Among Semantic Components in Embeddings. - Momose Oyama; Hiroaki Yamagiwa; Hidetoshi Shimodaira (EMNLP, 2024)

Predicting Drug-Gene Relations via Analogy Tasks with Word Embeddings. - Hiroaki Yamagiwa; Ryoma Hashimoto; Kiwamu Arakane; Ken Murakami; Shou Soeda; Momose Oyama; Mariko Okada; Hidetoshi Shimodaira (CoRR, 2024)

Mapping 1, 000+ Language Models via the Log-Likelihood Vector. - Momose Oyama; Hiroaki Yamagiwa; Yusuke Takase; Hidetoshi Shimodaira (ACL (1), 2025)

Revisiting Cosine Similarity via Normalized ICA-transformed Embeddings. - Hiroaki Yamagiwa; Momose Oyama; Hidetoshi Shimodaira (COLING, 2025)

Mapping 1,000+ Language Models via the Log-Likelihood Vector. - Momose Oyama; Hiroaki Yamagiwa; Yusuke Takase; Hidetoshi Shimodaira (CoRR, 2025)

Revealing Language Model Trajectories via Kullback-Leibler Divergence. - Ryo Kishino; Yusuke Takase; Momose Oyama; Hiroaki Yamagiwa; Hidetoshi Shimodaira (CoRR, 2025)

Likelihood Variance as Text Importance for Resampling Texts to Map Language Models. - Momose Oyama; Ryo Kishino; Hiroaki Yamagiwa; Hidetoshi Shimodaira (CoRR, 2025)