Papers by Kazuma Kobayashi
MED-COREASONER: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning (2026.acl-long)
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Fan Gao, Sherry T. Tong, Jiwoong Sohn, Jiahao Huang, Junfeng Jiang, Ding Xia, Piyalitt Ittichaiwong, Kanyakorn Veerakanjana, Hyunjae Kim, Qingyu Chen, Edison Marrese-Taylor, Kazuma Kobayashi, Akiko Aizawa, Irene Li
| Challenge: | Existing models that use English and local languages have a multilingual gap . a language-informed co-reasoning framework can be used to improve multilingual reasoning . |
| Approach: | They propose a language-informed co-reasoning framework that elicits parallel English and local-language reasoning and abstracts them into structured concepts. |
| Outcome: | Experiments show that Med-CoReasoner improves multilingual reasoning performance by 5% . the framework produces clinically sound and culturally grounded reasoning traces . |
A Case Study on Neural Headline Generation for Editing Support (N19-2)
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Kazuma Murao, Ken Kobayashi, Hayato Kobayashi, Taichi Yatsuka, Takeshi Masuyama, Tatsuru Higurashi, Yoshimune Tabuchi
| Challenge: | a news-aggregator is a website or mobile application that aggregates web content . dozens of professional editors manually create their headlines, which are much shorter than the original headlines. |
| Approach: | They propose a neural headline generation model that automatically generates short headlines from news articles. |
| Outcome: | The proposed model is deployed to an editing support tool and compares editors' behavior before and after the release. |
Extractive Headline Generation Based on Learning to Rank for Community Question Answering (C18-1)
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| Challenge: | Community question answering (CQA) forums do not always have appropriate headlines because of user-generated content. |
| Approach: | They propose an extractive headline generation method that extracts the most informative substring from each question as its headline. |
| Outcome: | The proposed method outperforms baselines including a prefix-based method . it uses the prefix of a question as its headline to create the most informative substring . |
Leveraging High-Resource English Corpora for Cross-lingual Domain Adaptation in Low-Resource Japanese Medicine via Continued Pre-training (2025.findings-emnlp)
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Kazuma Kobayashi, Zhen Wan, Fei Cheng, Yuma Tsuta, Xin Zhao, Junfeng Jiang, Jiahao Huang, Zhiyi Huang, Yusuke Oda, Rio Yokota, Yuki Arase, Daisuke Kawahara, Akiko Aizawa, Sadao Kurohashi
| Challenge: | low-resource language corpora in professional domains like medicine hinder cross-lingual domain adaptation of pre-trained large language models. |
| Approach: | They examine how linguistic features affect performance on a Japanese–English medical knowledge benchmark. |
| Outcome: | The proposed model can leverage English-language resources in medical domains while ensuring sufficient coverage of language-specific expressions in a target language. |