Papers by Yuma Tsuta
Tracing Multilingual Knowledge Acquisition Dynamics in Domain Adaptation: A Case Study of Biomedical Adaptation (2026.eacl-long)
Copied to clipboard
| Challenge: | Multilingual domain adaptation (ML-DA) enables large language models to acquire domain knowledge across languages. |
| Approach: | They propose an adaptive evaluation method that constructs multiple-choice QA datasets from the same bilingual domain corpus used for training. |
| Outcome: | The proposed method constructs multiple-choice QA datasets from the same bilingual domain corpus used for training, thereby enabling direct analysis of multilingual knowledge acquisition. |
uBLEU: Uncertainty-Aware Automatic Evaluation Method for Open-Domain Dialogue Systems (2020.acl-srw)
Copied to clipboard
| Challenge: | Existing evaluation metrics for text generation tasks do not consider uncertain responses without writing additional reference responses by hand. |
| Approach: | They propose a human-aided, uncertainty-aware evaluation method for open-domain dialogue systems, BLEU. |
| Outcome: | The proposed method is comparable to existing methods on Twitter and improves state-of-the-art evaluation method RUBER. |
Leveraging High-Resource English Corpora for Cross-lingual Domain Adaptation in Low-Resource Japanese Medicine via Continued Pre-training (2025.findings-emnlp)
Copied to clipboard
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. |