6 papers
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…
Likelihood Variance as Text Importance for Resampling Texts to Map Language Models
Momose Oyama, Ryo Kishino, Hiroaki Yamagiwa +1
We address the computational cost of constructing a model map, which embeds diverse language models into a common space for comparison via KL divergence. The map relies on log-like…
Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering
Yihua Zhu, Qianying Liu, Akiko Aizawa +1
Knowledge Base Question Answering (KBQA) aims to answer natural language questions using structured knowledge from KBs. While LLM-only approaches offer generalization, they suffer…
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…
Quantifying Lexical Semantic Shift via Unbalanced Optimal Transport
Ryo Kishino, Hiroaki Yamagiwa, Ryo Nagata +2
Lexical semantic change detection aims to identify shifts in word meanings over time. While existing methods using embeddings from a diachronic corpus pair estimate the degree of c…