Papers by Ryo Kishino
Quantifying Lexical Semantic Shift via Unbalanced Optimal Transport (2025.acl-long)
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| Challenge: | Existing methods for detecting semantic change only measure the level of individual usage instances. |
| Approach: | They propose to use unbalanced optimal transport to capture semantic change through excess and deficit in the alignment between usage instances. |
| Outcome: | The proposed method captures semantic change through excess and deficit in the alignment between usage instances. |
Likelihood Variance as Text Importance for Resampling Texts to Map Language Models (2025.findings-emnlp)
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| Challenge: | a language model map requires large text sets to be constructed . a resampling method reduces the number of texts needed while preserving accuracy of KL divergence estimates. |
| Approach: | They propose a method that selects important texts with weights proportional to log-likelihoods across models for each text. |
| Outcome: | The proposed method reduces the number of required texts while preserving the accuracy of KL divergence estimates. |
Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings (2026.findings-acl)
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| Challenge: | Fig. 1 and 2 shows that log-likelihood vectors provide a consistent representation for language models . weight permutation symmetries and architectural dependencies hinder direct comparisons between models with different learning methods or designs. |
| Approach: | They propose a log-likelihood vector for comparing language models as probability distributions . they establish a consistent scale for KL divergence across various settings . |
| Outcome: | The proposed model comparisons show that the log-likelihood space is smaller than the weight space . the proposed model compares language models across checkpoints, model sizes, quantization, fine-tuning, and layers . |