Papers by Momose Oyama
Understanding Higher-Order Correlations Among Semantic Components in Embeddings (2024.emnlp-main)
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| Challenge: | Independent Component Analysis (ICA) is an effective method for visualizing and interpreting the geometric structure of embeddings. |
| Approach: | They quantified embeddings' non-independencies using higher-order correlations and a maximum spanning tree of semantic components. |
| Outcome: | The results provide deeper insights into embeddings through ICA. |
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. |
Norm of Word Embedding Encodes Information Gain (2023.emnlp-main)
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| Challenge: | Distributed representations of words encode lexical semantic information, but what type of information is encoded and how? |
| Approach: | They propose to use the squared norm of static word embedding to encode the information gain conveyed by the word. |
| Outcome: | The proposed model can encode the information gain of a word in a language model or neural network. |
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. |
Revisiting Cosine Similarity via Normalized ICA-transformed Embeddings (2025.coling-main)
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| Challenge: | Existing studies on cosine similarity focus on the angle or correlation coefficient, but this study proposes a novel interpretation of the term word similarity. |
| Approach: | They propose a method for selecting statistically significant axes by deriving the probability distributions that govern each component and the product of components. |
| Outcome: | The proposed interpretation of cosine similarity is demonstrated through intuitive numerical examples and thorough numerical experiments. |
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 . |
Discovering Universal Geometry in Embeddings with ICA (2023.emnlp-main)
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| Challenge: | Existing studies have focused on achieving sparse embeddings or acquiring semantic axes, but this study focuses on the intrinsic independence present within embeddables. |
| Approach: | They propose to use independent component analysis to extract independent semantic components from pre-trained embeddings by leveraging anisotropic information that remains after the whitening process in Principal Component Analysis. |
| Outcome: | The proposed method reveals that embeddings can be expressed as a composition of a few interpretable axes and that these axe axe are consistent across languages, algorithms, and modalities. |