6 citations · 41 across the 52 of their papers we have counts for
56 papers
Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs
Zhihan Cao, Adam Nohejl, Hitomi Yanaka
Semantic relations organise word senses and are fundamental to lexical semantics. In order to evaluate semantic relation knowledge of large language models (LLMs), we propose a cha…
Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking
Xiaotian Wang, Youyuan Lin, Zhan Shen +1
Advanced large language models (LLMs) with long context windows can substantially reduce input truncation in document-level machine translation (DocMT). However, direct Doc2Doc tra…
(How) Do MLLMs Report Bistable Images Like Humans?
Ryota Takatsuki, Tomoki Doi, Amane Watahiki +2
Bistable images such as the duck-rabbit are classic stimuli in which one image supports multiple mutually incompatible interpretations, typically reported one at a time in humans.…
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…
Relation Geometry in Semantic Space of Language Models
Zhihan Cao, Hiroaki Yamada, Simone Teufel +4
When it comes to generating vector representations of words, current language models are achieving high-quality results. However, what is not known is the extent to which knowledge…
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…
YOMI-Bench: A Benchmark for Evaluating Kanji Reading and Phonological Understanding of LLMs for Japanese
Ryota Mibayashi, Hiroya Takamura, Hitomi Yanaka
We propose YOMI-Bench, a benchmark for evaluating kanji reading and phonological understanding of large language models (LLMs) for Japanese. In Japanese, a single kanji character o…
Beyond Monolingual Deep Research: Evaluating Agents and Retrievers with Cross-Lingual BrowseComp-Plus
Yuheng Lu, Qingcheng Zeng, Heli Qi +6
Deep research agents are increasingly evaluated on their ability to search for evidence, reason over retrieved sources, and produce grounded answers. Existing browsing benchmarks,…
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…
Analysis of the Neglect-Zero Effect in Large Language Models
Jin Tanaka, Daiki Matsuoka, Ryoma Kumon +1
We investigate the extent to which the language processing of LLMs resembles human cognitive processes, focusing on a human cognitive bias called the …
Sakura at BEA 2026 Shared Task 1: What Makes Vocabulary Difficult?
Adam Nohejl, Xuanxin Wu, Yusuke Ide +3
We describe two types of models for vocabulary difficulty prediction: a high-accuracy black-box model, which achieved the top shared task result in the open track, and an explainab…
Fine-Grained Analysis of Shared Syntactic Mechanisms in Language Models
Ryoma Kumon, Hitomi Yanaka
While language models demonstrate sophisticated syntactic capabilities, the extent to which their internal mechanisms align with cross-constructional principles studied in linguist…
Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers
Qingcheng Zeng, Yuheng Lu, Zeqi Zhou +6
Code-switching is a pervasive linguistic phenomenon in global communication, yet modern information retrieval systems remain predominantly designed for, and evaluated within, monol…
What Do Vision-Language Models Encode for Personalized Image Aesthetics Assessment?
Koki Ryu, Hitomi Yanaka
Personalized image aesthetics assessment (PIAA) is an important research problem with practical real-world applications. While methods based on vision-language models (VLMs) are pr…
Developing a Guideline for the Labovian-Structural Analysis of Oral Narratives in Japanese
Amane Watahiki, Tomoki Doi, Akari Kikuchi +5
Narrative analysis is a cornerstone of qualitative research. One leading approach is the Labovian model, but its application is labor-intensive, requiring a holistic, recursive int…
NeuronMoE: Neuron-Guided Mixture-of-Experts for Efficient Multilingual LLM Extension
Rongzhi Li, Hitomi Yanaka
Extending large language models to low-resource languages is essential for global accessibility, but training separate models per language is prohibitively expensive. Mixture-of-Ex…
Towards Automated Lexicography: Generating and Evaluating Definitions for Learner's Dictionaries
Yusuke Ide, Adam Nohejl, Joshua Tanner +3
Dictionary definitions are an essential resource for learning word senses, but manually creating them is costly. We thus study dictionary definition generation (DDG), i.e., the gen…
Investigating Training and Generalization in Faithful Self-Explanations of Large Language Models
Tomoki Doi, Masaru Isonuma, Hitomi Yanaka
Large language models have the potential to generate explanations for their own predictions in a variety of styles based on user instructions. Recent research has examined whether…
Interpreting Multi-Attribute Confounding through Numerical Attributes in Large Language Models
Hirohane Takagi, Gouki Minegishi, Shota Kizawa +2
Although behavioral studies have documented numerical reasoning errors in large language models (LLMs), the underlying representational mechanisms remain unclear. We hypothesize th…
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…
Enhancing Rating Prediction with Off-the-Shelf LLMs Using In-Context User Reviews
Koki Ryu, Hitomi Yanaka
Personalizing the outputs of large language models (LLMs) to align with individual user preferences is an active research area. However, previous studies have mainly focused on cla…
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…
Implementing a Logical Inference System for Japanese Comparatives
Yosuke Mikami, Daiki Matsuoka, Hitomi Yanaka
Natural Language Inference (NLI) involving comparatives is challenging because it requires understanding quantities and comparative relations expressed by sentences. While some app…
Can Large Language Models Robustly Perform Natural Language Inference for Japanese Comparatives?
Yosuke Mikami, Daiki Matsuoka, Hitomi Yanaka
Large Language Models (LLMs) perform remarkably well in Natural Language Inference (NLI). However, NLI involving numerical and logical expressions remains challenging. Comparatives…