5 papers
Cross-Lingual Representation Alignment by Token-Level Optimal Transport in a Language-Agnostic Space
Taisei Yamamoto, Ryoma Kumon, Danushka Bollegala +1
Cross-lingual alignment (CLA) aims to align the representations of large language models (LLMs) across languages, enabling cross-lingual transfer to improve multilingual capabiliti…
Evaluation of Multilingual Ability to Use Spatial Deictic Expressions in Vision-Language Models
Kaito Watanabe, Taisei Yamamoto, Tomoki Doi +1
One of the expected abilities of vision-language models (VLMs) is spatial reasoning ability based on a given text and image. To evaluate the spatial reasoning abilities of VLMs, we…
Revisiting the Systematicity in Negation in the Era of In-Context Learning
Hitomi Yanaka, Taisei Yamamoto
Understanding the meaning of negated sentences remains one of the challenges for language models, even in the era of large language models (LLMs). We analyze systematicity regardin…
Neuron-Level Analysis of Cultural Understanding in Large Language Models
Taisei Yamamoto, Ryoma Kumon, Danushka Bollegala +1
As large language models (LLMs) are increasingly deployed worldwide, ensuring their fair and comprehensive cultural understanding is important. However, LLMs exhibit cultural bias…
Bias Mitigation or Cultural Commonsense? Evaluating LLMs with a Japanese Dataset
Taisei Yamamoto, Ryoma Kumon, Danushka Bollegala +1
Large language models (LLMs) exhibit social biases, prompting the development of various debiasing methods. However, debiasing methods may degrade the capabilities of LLMs. Previou…