Papers by Haruki Sakajo
Tonguescape: Exploring Language Models Understanding of Vowel Articulation (2025.naacl-long)
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| Challenge: | a study shows that language models can explain vowel pronunciation based on tongue positions . a visual LM can explain the relationship between vowels and tongue positions, but it is unclear whether they align textual information with visual information. |
| Approach: | They created video and image datasets from MRI data to examine if LMs associate real tongue positions with vowel articulation. |
| Outcome: | The proposed model can explain vowel pronunciation and the correlation between vowels and tongue positions as textual knowledge. |
Dictionaries to the Rescue: Cross-Lingual Vocabulary Transfer for Low-Resource Languages Using Bilingual Dictionaries (2025.findings-acl)
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Haruki Sakajo, Yusuke Ide, Justin Vasselli, Yusuke Sakai, Yingtao Tian, Hidetaka Kamigaito, Taro Watanabe
| Challenge: | Existing approaches to cross-lingual vocabulary transfer face challenges when dealing with low-resource languages. |
| Approach: | They propose a dictionary-based crosslingual vocabulary transfer method that leverages bilingual dictionaries, which are available for many languages thanks to descriptive linguists. |
| Outcome: | The proposed method outperforms existing methods for low-resource languages. |
Measuring Linguistic Competence of LLMs on Indigenous Languages of the Americas (2026.eacl-short)
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| Challenge: | Existing benchmarks for linguistic knowledge of Indigenous languages of the Americas focus on high- and medium-resource languages with substantial digital presence. |
| Approach: | They propose a framework for probing large language models’ linguistic knowledge of Indigenous languages of the Americas using zero-shot prompting and few-shot probing. |
| Outcome: | The proposed framework evaluates models from five major families on 13 Indigenous languages including Bribri, Guarani, and Nahuatl. |
LoCt-Instruct: An Automatic Pipeline for Constructing Datasets of Logical Continuous Instructions (2025.emnlp-main)
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Hongyu Sun, Yusuke Sakai, Haruki Sakajo, Shintaro Ozaki, Kazuki Hayashi, Hidetaka Kamigaito, Taro Watanabe
| Challenge: | Existing instruction following datasets lack logical coherence across turns, narrow topical breadth and heavy manual effort. |
| Approach: | They propose a pipeline that leverages LLMs’ reasoning capabilities to assemble rich, topic-related single-instruction data into multi-turn dialogues and produce chains that are logically coherent, progressively deepen in content, and span diverse domains without fixed templates or extensive human annotation. |
| Outcome: | The proposed pipeline improves the performance of existing LLMs by integrating multiple topic-related data into multi-turn dialogues without fixed templates or extensive human annotation. |