4 citations · 4 across the 5 of their papers we have counts for
7 papers
Language Model Maps for Prompt-Response Distributions via Log-Likelihood Vectors
Momose Oyama, Yusuke Takase, Hidetoshi Shimodaira
We propose a method that represents language models by log-likelihood vectors over prompt-response pairs and constructs model maps for comparing their conditional distributions. In…
Measuring Affinity between Attention-Head Weight Subspaces via the Projection Kernel
Hiroaki Yamagiwa, Yusuke Takase, Hidetoshi Shimodaira
Understanding relationships between attention heads is essential for interpreting the internal structure of Transformers, yet existing metrics do not capture this structure well. W…
Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings
Ryo Kishino, Yusuke Takase, Momose Oyama +2
Log-likelihood vectors define a common space for comparing language models as probability distributions, enabling unified comparisons across heterogeneous settings. We extend this…
DeLTa: A Decoding Strategy based on Logit Trajectory Prediction Improves Factuality and Reasoning Ability
Yunzhen He, Yusuke Takase, Yoichi Ishibashi +1
Large Language Models (LLMs) are increasingly being used in real-world applications. However, concerns about the reliability of the content they generate persist, as it frequently…
Mapping 1,000+ Language Models via the Log-Likelihood Vector
Momose Oyama, Hiroaki Yamagiwa, Yusuke Takase +1
To compare autoregressive language models at scale, we propose using log-likelihood vectors computed on a predefined text set as model features. This approach has a solid theoretic…
Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed Embeddings
Hiroaki Yamagiwa, Yusuke Takase, Hidetoshi Shimodaira
Word embedding is one of the most important components in natural language processing, but interpreting high-dimensional embeddings remains a challenging problem. To address this p…
Continuous optimization by quantum adaptive distribution search
Kohei Morimoto, Yusuke Takase, Kosuke Mitarai +1
In this paper, we introduce the quantum adaptive distribution search (QuADS), a quantum continuous optimization algorithm that integrates Grover adaptive search (GAS) with the cova…