Papers by Yusuke Takase
Mapping 1,000+ Language Models via the Log-Likelihood Vector (2025.acl-long)
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| Challenge: | Existing methods to compare autoregressive language models are based on log-likelihoods . a model map is constructed using coordinates that capture the geometric structure of probability distributions based upon text-generation probabilities. |
| Approach: | They propose to use log-likelihood vectors to compare autoregressive language models . when treated as model features, their squared Euclidean distance approximates KL divergence . |
| Outcome: | The proposed method is highly scalable and easy to implement. |
Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed Embeddings (2024.findings-emnlp)
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| Challenge: | Embedding is an important component in natural language processing, but interpreting high-dimensional embeddings remains challenging. |
| Approach: | They propose a method which optimizes the order of axes in word embedding space by maximizing semantic continuity. |
| Outcome: | The proposed method improves the clarity of the word embedding space by maximizing the semantic continuity of the axes. |
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 . |