Papers by Makoto Shing
Focused Prefix Tuning for Controllable Text Generation (2023.acl-short)
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| Challenge: | Existing unannotated attributes could degrade models' performance . focus on the desired attribute can be achieved with focused prefix tuning . |
| Approach: | They propose focused prefix tuning to enable the control to focus on the desired attribute . they propose to reduce the number of unannotated attributes in a controllable text generation dataset . |
| Outcome: | The proposed approach achieves better control accuracy and text fluency than baseline models in single-attribute tasks. |
Release of Pre-Trained Models for the Japanese Language (2024.lrec-main)
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Kei Sawada, Tianyu Zhao, Makoto Shing, Kentaro Mitsui, Akio Kaga, Yukiya Hono, Toshiaki Wakatsuki, Koh Mitsuda
| Challenge: | democratization of AI aims to create a world where everyone can use AI . pre-trained models with high performance in Japanese are lagging in non-English-speaking communities . |
| Approach: | et al. released large-scale pre-trained models trained on large-data to improve access to AI . authors say the models are more accurate and more accurate than those trained in the English language . e-mail protected: email protected. |
| Outcome: | a new study shows that pre-trained models specialized for Japanese can achieve high performance in Japanese tasks. |