Ryo Kishino, Riku Shiomi, Hiroaki Yamagiwa, Momose Oyama, Hidetoshi Shimodaira.
Domain Mixture Design via Log-Likelihood Differences for Aligning Language Models with a Target Model.
Findings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026 Findings), to appear.
[arxiv]
Ryo Kishino, Yusuke Takase, Momose Oyama, Hiroaki Yamagiwa, Hidetoshi Shimodaira.
Establishing a Scale for Kullback–Leibler Divergence in Language Models Across Various Settings.
Findings of The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026 Findings), pp.23223–23248, San Diego, USA, July 2026.
[arxiv] [ACL Anthology] [github] [poster]
Momose Oyama, Ryo Kishino, Hiroaki Yamagiwa, Hidetoshi Shimodaira.
Likelihood Variance as Text Importance for Resampling Texts to Map Language Models.
Findings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025 Findings), pp.9453–9465, Suzhou, China, November 2025.
[arxiv] [ACL Anthology] [github]
Ryo Kishino, Hiroaki Yamagiwa, Ryo Nagata, Sho Yokoi, Hidetoshi Shimodaira.
Quantifying Lexical Semantic Shift via Unbalanced Optimal Transport.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), pp. 15913–15933, Vienna, Austria, July 2025.
[arxiv] [ACL Anthology]
[github] [poster]